Ramezani Amir
email: amir.ramezani.my@gmail.com

Dooraki

What Are Some of the Best Ways for a Machine to Learn?

Keywords: Artificial Intelligence, Supervised Learning, Reinforcement Learning, Unsupervised Learning, Machine Imagination, Machine Learning, Cognitive Development

Artificial intelligence recently had a great advancements caused by the emergence of new processing power and machine learning methods. Having said that, the learning capability of artificial intelligence is still at its infancy comparing to the learning capability of human and many animals. Many of the current artificial intelligence applications can only operate in a very orchestrated, specific environments with an extensive training set that exactly describes the conditions that will occur during execution time. Having that in mind, and considering the several existing machine learning methods this question rises that 'What are some of the best ways for a machine to learn?' Regarding the learning methods of human, Confucius' point of view is that they are by experience, imitation and reflection. This paper tries to explore and discuss regarding these three ways of learning and their implementations in machines by having a look at how they happen in minds.

Introduction

How minds work, or in another word how a human brain thinks, with the goal of implementing it in machines, is a long-term question in artificial intelligence. In the recent years, Artificial Intelligence algorithms have demonstrated outstanding progress and nowadays can be found in a variety of applications such as autonomous vehicles, computer games and health care automations. Having said that, the learning capability of artificial intelligence is still in its infancy comparing to the learning capability of humans. They can only operate in a very orchestrated, specific environments with an extensive training set that exactly describes the conditions that will occur during execution time. Systems of this kind are limited to the expertise and educated guesses of their human programmer; they lack the ability to learn in, or tune themselves to real-world environments autonomously or to employ learning in novel situations.

In order to create smarter machines, with an abstract similarity to human minds, design and development of complicated computational algorithms inspired by natural intelligence has already started. Among bioinspired algorithms those which simulated cognitive architectures are crucial where they are "the computational implementation of a cognitive model, and as such, constitute the substrate for all the cognitive functionalities in robots, like perception, attention, action selection, learning, reasoning, etc" [1]. As a result of these efforts, there are several successful methods of machine learning such as reinforcement learning, supervised learning and unsupervised learning where each group has its own branches: temporal difference and actor-critic methods for reinforcement learning, artificial neural network and its deep architectures for supervised learning and self-organizing and clustering methods for unsupervised learning. Saying that, this question rises that "What are some of the best ways for a machine to learn?" In this paper, I try to first address this question from a philosophical point of view and then I try to find the machine learning counterpart algorithms or in another word the way that a particular learning method is implemented in machines. Further, I try to discuss about the most important of learning method, considering this paper point of view, and the techniques and methods useful for its implementation in machines.

Learning Methodology

"By three methods we may learn wisdom: First, by reflection, which is the noblest; Second, by imitation, which is easiest; and third by experience, which is the bitterest"; Confucius. It is possible to find similar terminologies to each of the third ways of learning wisdom according to Confucius in machine learning field. "Third by experience, which is the bitterest": this is similar to 'the trial and error part of the reinforcement learning framework'. "Second, by imitation, which is easiest", this is similar to 'learning from demonstration or imitation learning in computer science'. Finally, "First, by reflection, which is the noblest", this important way of learning wisdom ends up creating new ideas and shaping new theories as a result of serious thought or consideration considering a person's ideological framework and according to what that he is learned through the Second and Thirds methods: imitation and experience.

By looking at the background of machine learning algorithms, one can see that supervised learning, unsupervised learning and reinforcement learning are studied and used by researchers for long time and in several cases, and they are still some of the important categories of learning in machines. Nonetheless, it is also possible to categorize them as following:

-Learning by Experience -Reinforcement Learning -Unsupervised Learning -Learning by Imitation -Learning From Demonstration • Pure Classification • Inverse Reinforcement Learning -Supervised Learning -Learning by Reflection -?

In the rest of this paper, I first explain briefly about the 'learning by experience' and 'learning by imitation' in machine learning field and later focus on the 'learning by reflection' and try to finds some references for it among machine learning algorithms.

Learning by Experience

Learning by trial and error (that is experience) is one of the foremost methods of learning specially for the sensorimotor development stage of intelligent creatures where they get master in moving their motors gradually by trying and fixing their errors. "By observing the development stages of an infant in terms of learning to articulate words, one can see that he first discovers how to control phonation, then focuses on vocal variations of unarticulated sounds, and finally automatically discovers and focuses on babbling with articulated protosyllables" [2]. In the following, it is explained how this method is implemented in machine learning community under different titles.

Reinforcement Learning

"RL algorithms address the problem of how a behaving agent can learn to approximate an optimal behavioral strategy, usually called a policy, while interacting directly with its environment" [START_REF] Barto | Intrinsic Motivation and Reinforcement Learning[END_REF]. Reinforcement learning family of algorithm are important in machine learning community for their high capability in decision making by learning from their own experiences. For an agent to be able to learn in an unknown environment, it should be able to autonomously interact with the environment. The framework of reinforcement learning is able to give the agent such an ability by letting it to take actions and measure the value of each action based on the reward it receives. More actions the agent takes, it gets more information about what is the best move in each state of the environment. Reinforcement learning [START_REF] Sutton | Introduction to Reinforcement Learning[END_REF] algorithms can be divided into several groups such as model-based and model-free, temporal difference methods, policy gradient methods and actor critic methods. Furthermore, they can be based on one or multi-step prediction [START_REF] Watkins | [END_REF]. An interesting trend in these algorithms is the idea of intrinsic moti-vation that makes the embodiment of qualities such as curiosity in machines possible.

The idea of 'learning by experience' in reinforcement learning frame work can be considered as a Markov Decision Process case. Where an agent interacts with its environment at discrete time steps and at each time step agent is located in a unique state according to its sensory input s t ∈ S. In each s t agent can take an action, a t ∈ A based on its current policy π(s t ). In the next time step, the state of agent changes to s (t+1) ∈ S based on its previous action and a reward R that it receives from the environment. (Figure 1).

Temporal Difference Approaches; Model-based and Modelless

Temporal difference algorithms are the reinforcement learning algorithms that try to calculate the value of a state or the values of each possible action in that state with attention to the reward received in each state. Furthermore, this information will be used by the agent to use in its policy in which it helps the agent to choose the best action in each state, and as a result the agent finally would be able to maximize its accumulated reward.

Q-Learning; Model-less Method

In many problems, there is no model of the environment. As a result, a model-free off-policy method such as q-learning [START_REF] Watkins | [END_REF] can be best to suite the scenario where it can be combined with experience replay method [START_REF] Lin | Reinforcement learning for robots using neural networks[END_REF] for designing more capable learning algorithms. A temporal difference reinforcement learning method can work for example based on an epsilon-greedy policy for action selection. The goal of this kind of policies is to achieve the maximum discounted reward over time and by using an iterative method of selecting the action with maximum value in each state-and updating this value using itself, the maximum action value in the next state and the reward it receives from the environment [START_REF] Sutton | Introduction to Reinforcement Learning[END_REF].

Dynamic Programing, Monte-Carlo and SARSA; A Modelbased Method

As it can be noticed by looking at the title of a modelbased reinforcement learning algorithm, its difference with model-less RL is in having a model of the environment. No matter, how the environment is, either 1D, 2D or 3D environment, or a mathematical formula of the dynamics of the system, a model based algorithm is able to calculate a future state of the environment with attention to the current state and the desired action. A very good example of a model-based algorithm is the work in [START_REF] Silver | [END_REF] where a machine is able to play Go game in a human level and win over many human players.

Function Approximator

Many reinforcement learning algorithms use the mentioned formula in order to find the optimal action value. However, there are two main problems, the lack of generalization and the problem of number of the states. In order to tackle these problems a function approximator an be used in order to find the optimal values of each action or state. As a result of using function approximators, Combination of supervised and unsupervised learning with reinforcement learning can be seen in several works. For example, combination of unsupervised learning (self-organizing map) and reinforcement learning can be seen in several works such as [8] where the input signals of the robot sensors are clustered using a dynamic variation of Kohonen self-organizing map algorithm [START_REF]Self-Organizing Maps[END_REF], and each cluster is defined to be an state for the reinforcement learning agent. As a result, the RL agent is able to control the robot movement based on pre-defined goals and rewards. Further, combination of reinforcement learning algorithm and supervised learning algorithms such as neural network can ends up creating a robust class of algorithm in terms of learning to achieve an optimal behavior similar to a human [START_REF] Mnih | [END_REF] in playing Atari game by defining a Deep Q-Network which is as a matter of fact combination q-learning and deep learning [11].

Actor Critic Approaches

Based on this view, reinforcement learning methods can be grouped under two categories. Actor-only methods and critic-only methods.

-"Actor-only methods work with a parameterized family of policies. The gradient of the performance, with respect to the actor policies, is directly estimated by simulation, and the parameters are updated in a direction of improvement." [12] -"Critic-only methods rely exclusively on value function approximation and aim at learning an approximate solution to the bellman equation, which will the hopefully prescribe a near-optimal policy." [12] Actor-critic methods [13], try to mix the important properties of actor-only and critic-only methods. The actor's policy parameters will be updated with the goal of improving the performance using the value function learned by critic through approximation architecture.

"The word actor and critic are synonyms for the policy and action-value function, respectively" [14]. One of the fresh important works in this area is the deep deterministic policy gradient [15] where it is an actorcritic network that is consist of two multilayer or deep artificial neural network based actor and critic. This algorithm could successfully solve the problem of continuous action control in some games and classic control problems.

Policy Gradient Methods

As mentioned in the previous section, actor-only methods are those in which the policy is parameterized and can be optimized directly. "The benefit of a parameterized policy is that a spectrum of continuous actions can be generated, but the optimization methods used (typically called policy gradient methods) suffer from high variance in the estimate of the gradient, leading to slow learning [12][16] [17][18] [19]." [14]

Hierarchical Reinforcement Learning Framework

Considering human behavior it "has long been recognized to display hierarchical structure: actions fit together into subtasks, which cohere into extended goaldirected activities" [20]. "Hierarchical reinforcement learning (HRL) decomposes a reinforcement learning problem into a hierarchy of sub problems or subtasks such that higher-level parent-task invoke lower-level child task as if they were primitive actions. A decomposition may have multiple levels of hierarchy" [START_REF] Hengst | Hierarchical Reinforcement Learning[END_REF]. This "decomposition into subproblems has many advantages. First, policies learned in subproblems can be shared (reused) for multiple parent tasks. Second, the value functions learned in subproblems can be shared, so when the subproblem is reused in a new task, learning of the overall value function for the new task is accelerated. Third, if state abstractions can be applied, then the overall value function can be represented compactly as the sum of separate terms that each depends on only a subset of the state variables. This more compact representation of the value function will require less data to learn, and hence, learning will be faster." [START_REF] Dietterich | [END_REF] 3.2 Unsupervised Learning Unsupervised learning method cannot be categorized under learning that happens by imitation since there is no output for the input to be imitated by machine, and it is hard to categorize it under learning that happens by experience perhaps because it does not have an attached reward to classify that particular experience as good or bad. However, this paper argues that receiving Fig. 2 Learning from Demonstration framework, Adapted from [START_REF] Argall | [END_REF] reward happens through many time steps where each time step can be considered as a new experience for our machine, that is each new sample has a subtle reward (loss) that tries to move a particular input toward a particular class. Thus it can be concluded that it is under the category of 'learning by experience'.

Learning by Imitation

The learning capability of human kind develops throughout his life by getting master in 'learning by imitation' (e.g. learning how to write a word from the first grade teacher), a wide spectrum of what we learn after our early ages comes from this type of learning.

Learning From Demonstration (Apprenticeship Learning)

Learning from Demonstration is a natural and intuitive machine learning approach where an agent that called apprentice tries to learn a behavior from demonstration of another agent called the expert. "We simply show the robot how to achieve a task. This has the immediate advantage of requiring no (or very little) specialized skill or training, and makes use of a human demonstrator's existing procedural knowledge useful to identify which control program to acquire" [24]. A very good example for learning from demonstration, is how to fly an airplane, considering all the different possibilities exist for using the buttons and handles in the cabin of an airplane, the best way to learn how to fly is by observing an expert. Perhaps it is just impossible to try to learn to fly an airplane simply by random pushing of buttons. Looking at the literatures there are at least two methods to deal with this problem of Apprenticeship Learning (AL); Pure Classification and Inverse Reinforcement Learning (IRL)

Pure Classification

Pure classification, for instance, the large margin method [START_REF] Ratliff | Proceedings of the 2007 7th IEEE-RAS International Conference on Humanoid Robots[END_REF] has several advantages and also some disadvantages that is explained in the following; It is "easy to implement, fast, no need to resolve MDPs, no need to do a choice of features thanks to boosting techniques. However, they do not take into account the structure of the MDP because the temporal structure of the expert trajectories is not used in a pure classification method. To tackle this drawback, the authors of [START_REF] Melo | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6322 LNAI[END_REF] use a kernel-based approach to encapsulate the structure of the MDP into the classifier, which needs the calculation of the MDP metrics and thus the knowledge of the whole dynamics." [START_REF] Piot | Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems (International Foundation for Autonomous Agents and Multiagent Systems[END_REF]

Inverse Reinforcement Learning

Inverse Reinforcement Learning (IRL), introduced in [START_REF] Russell | Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT)[END_REF] and formalized in [START_REF] Ng | Proceedings of the Seventeenth International Conference on Machine Learning[END_REF], is related to Apprenticeship Learning (AL) family of algorithm. However, IRL searches to find a reward where this reward should be able to explain the expert behavior and not the expert policy. "The key idea behind IRL is that the reward may be the most succinct hypothesis explaining the expert behavior. Some algorithms [START_REF] Abbeel | Proceedings of the 21st International Conference on Machine Learning (ICML)[END_REF][31] [32] use IRL as an intermediary step to find a policy but other algorithms [33] [34] are "pure" IRL algorithms and output a reward. This reward must then be optimized via a direct reinforcement learning algorithm." [START_REF] Piot | Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems (International Foundation for Autonomous Agents and Multiagent Systems[END_REF] 4.4 Supervised Learning This famous method of machine learning helps the machine to learn to imitate that if a particular input received what should be the particular output. As a result, in this paper it is considered to be under the category of 'learning by imitation'.

Learning by Reflection

If we describe 'learning by reflection' as the result of serious thought and consideration which can cause for example the creation of a scientific discovery or theory, then it can be explained as a logical outcome of the following cognitive activities:

Reasoning (Deduction and Induction)

Many of the scientific (e.g computation, biological, social) theories are as a result of a chain of reasonings. Where one can interpret reasoning as an unsupervised or supervised pattern recognition, where for instance by seeing a view a person can recognize it or categorize it as a special event or group, and as a result it can be categorized under the 'learning by experience' or 'learning by imitation'. Reasonings help us to understand and being able to explain where an incident is coming from or where it will ends up or what is the meaning of a particular perception (e.g. a view, a sound, an smell or a combination of several senses (biological sensors)). Furthermore, following the trajectory of incidents (started by incident 'a') using chain of reasonings one can end up to a particular incident 'b' and form a social or scientific theory, where it begins by incident 'a' and ends up to incident 'b'. For example, a person look is faint, we can reason that its because he is hungry or sick, let say the person is hungry thus he does not have money or is very lazy to buy food, or let say the person is sick so he does not have money to buy food or he got a virus, perhaps his immune system is weak or the virus is new, and this chain of reasoning can continue to reach a particular theory based on the look of a person.

Imagination

When a person imagines, he creates or shapes a perception in his mind, which has been obtained according to what he has perceived previously (either through experience or imitation) with attention to his current thoughts and ideas; this imagination can be altered or enhanced with-in the framework of his believes (e.g. a scientific belief or a religious belief). Here, I emphasize that even though machine imagination concept that is discussed in [35] and similar works are interesting but they are not the topic that this paper intends to discuss about. In those works imagination for machine is discussed and explained as a process that convert an input, for example a text, to an image where the process and type of learning can be categorized more under the category of 'learning by imitation' (learning to produce a particular image when input is a particular text or sentence). Nonetheless, what this paper means by "imagination" in a machine is as a matter of fact a process that uses machine memory and knowledge in order to create new perceptions that did not shown to machine before and in order to learn from this new perception.

This paper categorizes imagination into the following categories:

-The first category is a synthesization of world model, based on the past experiences, for example, imagine a person that seen a place before, he is able to imagine this place, or world model, again in his mind and take new actions and calculates new results accordingly. In computer science there are instances of this kind of imagination, for instance SLAM [36], where a machine learn a model of the world and as a result imagines this model and particular outcomes with-in this imaginary model of the world according to particular actions. Model-based reinforcement learning algorithms are among those in which a model of the world can be built, and then refined and completed using experiences and observations. Model-based RL aims to endow an agent "with a model of the world, synthesized from past experience. By using an internal model to reason about the future, here also referred to as imagining, the agent can seek positive outcomes while avoiding the adverse consequences of trial-and-error in the real environment including making irreversible, poor decisions" [37]. One important consideration in this type of imagination is that it is limited by the model of the world, made by observation, that is, this kind of imagination cannot go beyond the observation of person or machine. In another world, this imagination is limited to experience and observation and it is kind of learning by experience. Thus, perhaps it is possible to think that model-based thinking (method) is a reasoning process which of course can reach to very important discoveries and outcomes. -In order to explain the second category, first, let's imagine Einstein thinking about time being relative, he is considering that his bus can move faster than a beam of light coming from the clock tower, this idea perhaps is not based on experience or observation (it is more than a model-based method). He was imagining something out of the world-model he experienced by creating at the first point an exception beyond or contradictory to the experiences and observations he had because nobody can move faster than light so it can not be experienced nor it can not be imitated, however, this way of thinking (exceptional or contradictory) paved the way to create a great theory. Furthermore, following the chain of reasonings it is possible to reach a point that the person does not have any experience or imitation information about it, for example considering biological advancements and discovery of a method that makes human im-mortal, the fact of immortal human perhaps is not experienced before, it can be imitated from the movies, but for the first time there was no movie about it, this example is also an imagination that is based on pure imagination contradictory or beyond experiences and imitations.

Is it possible to implement the 'learning by reflection' in machines? As explained in this section, a reflection can be explained as an imagination that is not limited to experience or imitation. Chain of reasonings and limited imagination both can be explained by 'learning by experience' or 'learning by imitation', however, a reflection, needs an elaboration, enhancement, or exception beyond the experienced world-model. This enhancement would happen according to the believe frame-work of the machine or person where this belief frame work can be scientific, religious or spiritual. Further, this enhancement can happen using what learned by imitation or by mixing different experiences into each other and judge the possibility of the result according to the believe framework of the person or machine.

Conclusion

In this paper, several ways of learning for a machine -learning by experience, learning by imitation and learning by reflection-inspired by human ways of learning wisdom explained by Confucius, introduced and discussed. Further, it explained that two of these methods, 'learning by experience' and 'learning by imitation' are widely researched and implemented in machine learning community under other titles. For example, reinforcement learning and unsupervised learning are based on 'learning by experience' and learning from demonstration and supervised learning are based on 'learning by imitation'. Nevertheless, the third way of learning wisdom, 'learning by reflection', is focused less and less implemented as well in machine learning community and algorithms. As a result, this paper discussed about this method of learning and the ways it can be explained through them in minds and machines.

Fig. 1

 1 Fig. 1 Reinforcement Learning Framework


Olivier Provini

Negotiating the Marketization of Higher Education

Keywords: Higher education, policy analysis, domestic actors, Tanzania, postsocialism, Kenya, neopatrimonialism

  L'archive ouverte pluridisciplinaire

Introduction

Since the 2000s, international organizations and donor agencies have been heavily promoting and financing reforms in the higher education sector. The World Bank is one of the principal donors and its experts have formulated recommendations mentioning public and private universities as leading institutions for sustained economic growth. Higher education is more and more considered as a priority sector linking economic growth and democratization processes in development countries. By producing a well-trained workforce, which is adapted to the needs of the market, higher education is shaped not only as a step towards a prosperous national economy but also towards the formation of a middle class (World Bank 1988[START_REF] Japhet | Varsity students storm Ministry, press for fare[END_REF][START_REF]A chance to learn: Knowledge and finance for education in sub-Saharan Africa[END_REF]2009;World Bank and Unesco 2000;[START_REF] Collins | The World Bank, support for universities, and asymmetrical power relations in international development[END_REF]. This focus on university reforms presents a major shift away from the experts' previous recommendations and priorities. During the 1980s and the 1990s, the Bank's experts have targeted the fields of primary and secondary education in Africa, instead of the university sector [START_REF] Brock-Utne | Formulating higher education policies in Africa: The pressure from external forces and the neoliberal agenda[END_REF]. National governments were encouraged to channel their investment into elementary education, while higher education was sometimes considered as expensive and "luxury" (World Bank and Unesco 2000, p. 14). Government leaders were advised to disengage from the university sector and to promote the intervention of private investors instead. These policies are often considered as the beginning of the "privatization" of public higher education institutions [START_REF] Oketch | The growth of private university education in Kenya: The promise and challenge[END_REF][START_REF] Berthelemy | Privatisation en Afrique subsaharienne : un état des lieux[END_REF][START_REF] Ishengoma | Cost-sharing in higher education in Tanzania: Fact or fiction?[END_REF][START_REF] Moja | Policy responses to global transnational by African higher education systems[END_REF][START_REF] Sall | Alternative models to traditional higher education: Market demand, networks, and private sector challenges[END_REF][START_REF] Abagi | New Trends in Higher Education. Private Higher Education in Kenya[END_REF]Mwiria et al. 2006;[START_REF] Mabizela | Surge amid public dominance in higher education: The African perspective[END_REF][START_REF] Oanda | Privatisation and private higher education in Kenya: Implications for access, equity and knowledge production[END_REF]. Being a polysemous notion [START_REF] Musselin | Vers un marché international de l'enseignement supérieur?[END_REF], the concept is used in this text to highlight the differences, which exist in the processes and outcomes of higher education reforms in Kenya and Tanzania. Whereas the University of Nairobi (UoN) in Kenya is mainly based on private funds, the Tanzanian government remains the principal sponsor of the University of Dar es Salaam (UDSM). More generally, in the higher education sector, privatization processes inform about transforming state-university relations and changing power balances between stakeholders, especially in periods of economic crisis. Given the financial restrictions in both countries during the 1980-1990s, public authorities were eager to diversify the resources by attracting private funding. The introduction of cost-sharing policies, which shifted the responsibility for the university fees from the government towards parents and students [START_REF] Ishengoma | Cost-sharing in higher education in Tanzania: Fact or fiction?[END_REF], has been defined as a prime tool to attract alternative funding for the higher education sector. In fact, both the Tanzanian and Kenyan reform makers have promoted the introduction of university fees. However, only in the Kenyan case have the cost-sharing policies led to an increasing involvement of private funds, whereas the higher education sector in Tanzania continues to be financed by public funds. By adopting a comparative perspective, one of the objectives of this article is to understand the driving factors behind these different outcomes.

Higher education reforms in the Global South have attracted the interest of academics, especially in the fields of educational science and economics [START_REF] Galabawa | Funding, selected issues and trends in Tanzania higher education[END_REF][START_REF] Kajubi | Financing of higher education in Uganda[END_REF][START_REF] Lebeau | The dilemma of post-colonial universities. Elite formation and the restructuring of higher education in sub-Saharan Africa[END_REF][START_REF] Johnstone | Higher education finance and accessibility: Tuition fees and student loans in sub-Saharan Africa[END_REF][START_REF] Zeleza | African universities in the twenty-first century. Volume I: Liberalisation and internationalization[END_REF][START_REF] Assie-Lumumba | Higher education in Africa. Crises, reforms and transformation[END_REF][START_REF] Charlier | Savants et sorciers. Les universités africaines francophones face à la prétendue universalité des critères de qualité[END_REF][START_REF] Mamdani | Scholars in the Marketplace: The Dilemmas of Neo-Liberal Reform at Makerere University[END_REF][START_REF] Charton | De l'intellectuel à l'expert. Les sciences sociales africaines dans la tourmente: le cas du Kenya[END_REF][START_REF] Marcucci | Higher educational cost-sharing, dual-track tuition fees and higher educational access: The East African experience[END_REF][START_REF] Wangenge-Ouma | Higher education marketisation and its discontents: The case of quality in Kenya[END_REF][START_REF] Chouli | Le néolibéralisme dans l'enseignement supérieur burkinabé[END_REF]Makosso 2009;[START_REF] Berhanu | Research universities in the Nile basin countries[END_REF][START_REF] Pillay | Higher education financing in East and Southern Africa[END_REF][START_REF] Bugwabari | Universités, universitaires en Afrique de l'Est[END_REF][START_REF] Tade | Beyond reforms: The politics of higher education transformation in Africa[END_REF][START_REF] Oketch | Financing higher education in sub-Saharan Africa: some reflections and implications for sustainable development[END_REF]. Donors and international institutions provided funds for research and thus encouraged the establishment of an innovative field of action-oriented research. The emerging literature on case studies in specific countries is characterized by an intertwining of fundamental research and expert's recommendations. For instance, the experts of the Partnership for Higher Education in Africa (PHEA), which consists of a network of several US foundations (Carnegie Corporation of New York, the Ford Foundation, the John D. and Catherine T. MacArthur Foundations, the Rockefeller Foundation, the William and Flora Hewlett Foundation, the Andrew W. Mellon Foundation and the Kresge Foundation), have conducted empirical research in different East African universities, by providing an overview of their management and their financial situation as well as recommendations to increase the quality of public and private services and to formulate solutions for identified problems [START_REF] Cooksey | Higher education in Tanzania. A case study[END_REF][START_REF] Musisi | Makerere university in transition 1993-2000: Opportunities and challenges[END_REF]Mwiria et al. 2006). Barriers between the scientific and the expert literature are further blurred by African scholars, managers, deans or vice-chancellors who are directly involved in the different reform processes, for instance, in Tanzania and Kenya [START_REF] Luhanga | Higher education reforms in Africa: The University of Dar es Salaam experience[END_REF][START_REF] Kiamba | Privately sponsored students and other income-generating activities at the University of Nairobi[END_REF][START_REF] Kimambo | search of relevance: A history of the University of Dar es Salaam[END_REF][START_REF] Luhanga | The courage for change. Re-engineering the University of Dar es Salaam[END_REF][START_REF] Gichaga | Surviving the academic arena. My complex journey to the apex[END_REF].

Even if this literature on the ongoing higher education reforms is prolific, the scientific discussion converges in an implicit and recurrent argument about the drivers of these policies. Given the increasing involvement of international experts in the policy design, university reforms are often described as the output of the incentives of international stakeholders and the representatives of international organizations, notably the World Bank with the coercive instruments of Structural Adjustment Programmes. The underlying assumption, which is rarely explicitly asserted however, would be that the more countries, in which these policies are conducted depend on international aid, the more reform processes are influenced and guided by external stakeholders. The forms and effects of the influence of international experts on higher education reforms is rarely questioned, nor is the success of these reforms. For instance, the literature on university policies generally confirms the achievement of privatized higher education institutions.

This statement, however, is not limited to the higher education sector, but can be extended to the literature on policy making in Africa, more generally. In fact, previous research on policies in different sectors have stressed the orienting function of donors and external experts in the making of reforms [START_REF] Ambrosetti | Nouvelles normes, nouveaux espaces de jugement: la valeur légitimatrice de l'ONU et ses effets normatifs[END_REF][START_REF] Harrison | Neoliberal Africa. The impact of global social engineering[END_REF][START_REF] Zoomers | The globalisation and the foreignisation of space : Seven processes driving the current global land grab[END_REF][START_REF] Edwards | Toxic Aid. Economic Collapse and Recovery in Tanzania[END_REF][START_REF] Lofchie | The political economy of Tanzania. Decline and recovery[END_REF][START_REF] Eboko | Repenser l'action publique en Afrique[END_REF][START_REF] Fouilleux | Au-delà des Etats en action… La fabrique des politiques publiques globales[END_REF]. This article deals with this assumption as a proper research question. We thereby argue that the predominant role of international stakeholders in policy processes should not remain an implicit statement but presents a substantive hypothesis for academic discussion, especially in political science and policy analysis. Taking the example of the higher education sector, the aim of this paper is to analyze the reform processes in two East African universities by focusing, more specifically, on the configuration of stakeholders. We provide insights into complex multi-level negotiations of university reforms and into the historical evolution of the policy sector. Whereas the literature on public processes in African countries in the higher education sector, has been focusing on the influential power of international stakeholders, such as donors and experts, only few authors have taken into account the impact of and the role played by domestic actors. The aim is not to reject the argument of internationally driven public policy processes per se. Rather, we seek to nuance a dominant assertion in the debates on higher education reforms, by bringing in a hitherto disregarded variable, especially the role played by administrative and academic staff and students. Rather than evaluating the success or the failure of the reforms, this paper aims to highlight the powerplays between a various set of domestic actors who reshape the outcomes of higher education policies. However, stakeholders are not the only variable explaining policy processes. Their interactions and negotiations take place in a specific historical and political context and are framed by the legacy of the policy sector. Our demonstration is based on a theoretical framework combining a sociological approach of policy analysis [START_REF] Hassenteufel | L'Etat mis à nu par les politiques publiques?[END_REF][START_REF] Hassenteufel | Sociologie politique: l'action publique[END_REF][START_REF] Boussaguet | Une "french touch" dans l'analyse des politiques publiques?[END_REF] and historical state sociology [START_REF] Bayart | The state in Africa. The politics of the Belly[END_REF]Ferguson 2006;[START_REF] Lund | Twilight institutions: Public authority and local politics in Africa[END_REF][START_REF] Bellagamba | Beside the state. Emergent powers in contemporary Africa[END_REF]. We argue that policy making and implementation are framed by political culture and the historical path of the regime in place [START_REF] Martin | Tanzanie. L'invention d'une culture politique[END_REF][START_REF] Médard | Etats d'Afrique noire. Formations, mécanismes et crise[END_REF][START_REF] Bjerk | Sovereignty and socialism in Tanzania: The historiography of an African state[END_REF][START_REF] Branch | Kenya. Between Hope and Despair, 1963-2011[END_REF][START_REF] Bach | Neopatrimonialism in Africa and beyond[END_REF][START_REF] Fouéré | Julius Nyerere, Ujamaa, and political morality in contemporary Tanzania[END_REF]. The political regimes of Tanzania and Kenya have been shaped, respectively, by socialist and neopatrimonial politics and represent exemplary cases to study the impact of political culture on the negotiation of higher education policies. Therefore, this paper crosses the experiences from the reform processes of the UDSM in Tanzania and the UoN in Kenya.

Both universities are the first public higher education institutions of their country and are often used as pilot projects for reform processes1 . The interest in comparing the Kenyan and the Tanzanian cases is founded on three main arguments. Firstly, the UDSM and the UoN share a part of their institutional history, especially through the creation of the federal University of East Africa during the colonial period, and have thus produced common administrative heritage and bureaucratic routine [START_REF] Mngomezulu | A political history of higher education in East Africa. The rise and fall of the University of East Africa, 1937-1970[END_REF]. Secondly, the collaborations between these two universities have been further stimulated by the East African Community (EAC) and its main institution for higher education, the Inter-University Council for East Africa (IUCEA). This council aims to facilitate regional policies and to standardize practices and processes within the higher education institutions. It is thus stimulating to analyze, if the reform processes in each country reveal similar and standardized stakeholder negotiations and actor configurations. Thirdly, the higher education sectors in Kenya and Tanzania have followed different trajectories since Independence. Whereas in Tanzania, education policies are still seen as a public service and are strongly regulated by state agents, the Kenyan sector has been shaped by the intervention of private actors and commercial mechanisms. By studying the reforms against the background of the political and historical specificities of each country, it will be interesting to trace back the driving forces of these diverging policy configurations as well as the outcomes of the privatization processes.

The article is based on the results of a PhD dissertation in political science (author 2015a), comparing higher education reforms in East Africa (University of Dar es Salaam, University of Nairobi, University of Makerere in Uganda, University of Burundi in Burundi). The data for this paper was collected during a 12-months fieldtrip between February 2010 and October 2012. We conducted around 69 semi-structured interviews with various types of stakeholders involved in the reform processes (students, lecturers, administrative staff, political leaders on the regional and national levels, funding partners, international organizations). The economic data specifying the universities' budgets stems from a documentary research in the financial and administrative departments of the Universities of Dar es Salaam and Nairobi. The data about the historical evolution of higher education policies in each country was completed by a press review including several national newspapers (e.g. The East African, Daily News, The Citizen, The Guardian, Daily Nation, The Economic Review and The Weekly Review) and covering the period from 1980 to 2013.

The paper is organized in two sections, while each of it treats one case study. Firstly, we demonstrate how the privatization of higher education is negotiated at the UDSM in Tanzania. Despite the weight of the economic dependence on international actors, the reforms are reoriented by a heterogeneous set of domestic actors, especially students, in a post-socialist regime. The second part of the article focuses on the Kenyan case and the reforms at the UoN. It reveals the decisive role played by members of the political elite and the administrative and academic staff at the UoN, shaping the process of privatization in a context of neopatrimonial politics.

Mobilizing against the cost-sharing policy in a post-socialist regime: the key role of students during the implementation of a higher education reform at the UDSM in Tanzania Since the 1970s, Tanzania has been attracting the attention of the international donor community by receiving the biggest part of development assistance in Sub-Saharan Africa. For instance, between 2001 and 2009 (Edwards 2014, p. 53), the Tanzanian government has obtained more than 8% of the global amount of aid allocated to countries in Sub-Saharan Africa. The financial flows coming from outside represent 25% of the gross national income of Tanzania between 1990and 1994and 13.5% between 1995and 2005(Harrison et al. 2009, p. 272). The decision of international institutions to target Tanzania as a recipient country has led several authors and development experts to consider Tanzania as a "donor darling" [START_REF] Harrison | Tanzania: A genuine case of recipient leadership in the aid system?[END_REF][START_REF] Nord | Tanzania. The story of an African transition[END_REF][START_REF] Edwards | Toxic Aid. Economic Collapse and Recovery in Tanzania[END_REF][START_REF] Lofchie | The political economy of Tanzania. Decline and recovery[END_REF]. The involvement of external stakeholders and experts in the financing of development activities might suggest an increasing influence of these international actors on the design and the implementation of policies. A closer look on the negotiation of higher education reforms in Tanzania however, indicates that policies are also shaped by a heterogeneous set of domestic actors. This section offers insights into the role of students in the renegotiation of externally driven policy reforms. It is crucial to consider the impact of these domestic stakeholders by taking into account the historical path of the higher education sector and the political culture, which was shaped by Julius K. Nyerere and post-socialist state-building.

At the Independence, the Tanzanian higher education policy was framed on the socialist ideology of J. K. Nyerere, the father of the nation and a hero of the postcolonial era [START_REF] Charton | Présentation. Héros nationaux et pères de la nation en Afrique. Vingtième siècle[END_REF][START_REF] Fouéré | Tanzanie: la à l'épreuve du post-socialisme[END_REF][START_REF] Fouéré | Julius Nyerere, Ujamaa, and political morality in contemporary Tanzania[END_REF][START_REF] Fouéré | Remembering Julius Nyerere in Tanzania: History, legacy, memories. Dar es Salaam[END_REF][START_REF] Becker | Remembering Nyerere: Political rhetoric and dissent in contemporary Tanzania[END_REF]author 2015b). Higher education policies have been one of the target sectors to develop and to enforce Nyerere's "socialist attitude of mind" (Nyerere 1962, p. 1), a prerequisite for building a newly independent society and for creating common citizen subjectivities [START_REF] Ranger | The "new historiography" in Dar es Salaam: An answer[END_REF]. In fact, education was considered as a one of the principal tools to achieve Nyerere's main goal, which was economic and political selfreliance [START_REF] Nyerere | Freedom and socialism/uhuru na ujamaa: A selection from writings and speeches 1965-1967[END_REF][START_REF] Hargreaves | The idea of a colonial university[END_REF][START_REF] Bienen | Tanzania. Party Transformation and Economic Development[END_REF]Robertson 1979;[START_REF] Shivji | Intellectuals at the hill. Essays and talks 1969-1993[END_REF][START_REF] Ivaska | Of students, "nizers", and a struggle over youth: Tanzania's 1966 national service crisis[END_REF][START_REF] Ivaska | Cultured states: Youth, gender, and modern style in 1960s Dar es Salaam[END_REF]. Nyerere and his government asserted that the university had to be accessible, developmental and at the same time contribute to nation building. This aim, of using the university as a step of creating a unified and independent Tanzanian nation, was reflected in the curricula, the research programs, the moral code and the philosophy of the higher learning institution, more generally [START_REF] Schutte | The university of Dar es Salaam: A socialist enterprise[END_REF]. The national university was considered as a crucial tool for emancipation. As the newly independent state depended heavily on expatriates to staff the middle and upper cadres of the civil service, the government implemented a new training program based on higher education to "Africanize" the workforce (Lema et al. 2004, p. 18). By making the access to public amphitheaters free for all students, the government attempted to increase student enrollment and to pursue the "Tanzanianization" of the national elite. Whereas tuition fees had to be paid until 1967, the government decided to grant scholarship to all students after the official adoption of the African socialism during the Declaration of Arusha. In 1974, the government even abrogated the bursary system and completely took over the whole university expenditures (Ishengoma 2004, p. 105). A former student at the UDSM and current professor remembered this period, when students could access the university without paying any fees: "In my first years, as soon as I arrived in the campus, all was free [laughs]. I went to the cafeteria, I went to the bookshop for free […]. All books were paid by the university even if the books, that we bought, were outside the university, in the city. You gave your receipt, and they paid back your books. It [was] the responsibility of the government to educate all people"2 .

However, during the period between 1975 and 1985, the higher education sector faced a serious financial crisis in terms of both operating and development budgets. In response, the Tanzanian government turned to private revenue to cover the public expenditures. A cost-sharing policy, whereby costs were partitioned between governments, parents and students, was implemented in three gradual phases. In phase 1 (1992)(1993), students and parents had to cover transportation, application registration, examination and union fees; in phase 2 (1993)(1994), they were in charge for food and accommodation only and during the last phase (2004)(2005), they were requested to pay tuition and examination fees, books, stationary costs and medical insurance (Ishengoma 2004, p. 105-106). Government officials and members of the academic staff generally associate the implementation of the cost-sharing policy with the impact of international recommendation, especially of the World Bank, to decrease public investments and expenditures. According to a professor at the UDSM, these reforms broke with the socialist path of university policies and reflected the increasing impact of the interests of the international donors: "University reforms, you know, try to change the traditional role of the university. It is a movement, which did not start from Tanzania. It is a movement, which is really brought to the government by the World Bank and the other institutions. They have imposed the reforms here. The reforms are not a negative thing. The reforms are not a bad idea […]. The problem, with these reform processes, is that they came from an agenda of outside"3 . This interpretation of externally controlled higher education reforms is shared by numerous members of the UDSM. The World Bank's recommendations are often considered as a driving force of the privatization policies. A project coordinator and intermediary between the World Bank and the Ministry of Higher Education underlined the role of the Bank's experts in various reform processes: « number one, we must send it [official document] to the World Bank. They can pass it with their education experts and they can approve the final version […]. Sometimes they [Tanzanian cabinet members] do not have ideas themselves, so they consult some international experts who are in the fields. When we search some consultants and after the advertisement, all steps are checked and approved by the World Bank, step-by-step, step-by-step. When there is the presentation of an inception report […], we also invite the World Bank. They have experts in several sectors, experts in education economy »4 . Given Tanzania's new status as an international donor darling, one could suggest that the design and implementation of university policies are more and more controlled by external experts aiming for the retreat of the state from the regulation of the sector and the reduction of public expenditures. However, empirical evidence invites us to nuance this top-down perspective and to consider the role of domestic actors and dynamics during the implementation phase. Despite the application of the cost-sharing policy, the registration rate of private students5 at the UDSM has remained relatively low. Private students only represent around 7% of the enrolled students in 20146 . Even though the cost-sharing reform was introduced in the 1990s to encourage the withdrawal of the state from the higher education sector, the Tanzanian government remains the principal funder of the UDSM (author 2015a, p. 414).

Table 1: Evolution of the UDSM budget, 1998UDSM budget, -2009 In fact, public investment in higher education has not decreased since the 1990s. Despite the policy turn towards a privatized university system, promoting the financial withdrawal of the state, the government never ceased to be the principal sponsor of the UDSM. The next sections aim to resolve this seeming paradox by scrutinizing the implementation and the renegotiation of the reforms by domestic actors with a special focus on student mobilizations. Students have historically negotiated the implementation of university policies by mobilizing the memory of a higher education sector, which was shaped by the socialist ideology of Nyerere's government. Through their mobilizations, students have recast the idea about the free and unlimited access to public education as a precondition for democratization and self-reliance.

The student organization DARUSO (Dar es Salaam University Students Organization) plays a particular role in the higher education sector by setting up demonstrations and by formulating specific claims. A recurrent request of DARUSO is to abandon the cost-sharing policy, promoted by the experts of international institutions, such as the World Bank. Since the implementation of the reform in the early 1990s, DARUSO has been leading a series of demonstrations on the campus (April-January 1991, January-February 1992, April-May 1994, March 2005, August-October 2006, January-May 2007, November 2008, January-February 2011, December 2011-January 2012), claiming for a full coverage of the student's fees by the government (Kulekana 1990, p. 1;Daily News 1991, p. 1;Daily News 1992, p. 1;Japhet 1994, p. 3;Cosato 2002, p. 2;Daily News 2006, p. 2;Mwasumbi 2006, p. 3). According to a DARUSO leader, mobilizations are a crucial tool to build a power balance between the students and the government, during the reform process: "We think that [student] crises are good for university. So, the university has known some crises in several years. Almost years, we have seen student crises between students and the government. Normally, the crises have been caused by the implementation of one policy, which affect the welfare of students […]. Our role, always, is to record the claims of the students. And these crises happen because the government and the administration do not answer to these claims […]. We know that strikes are not the best solution. It is only the last solution in the negotiations with the government" 7 . DARUSO's aim is to influence the negotiations about the cost-sharing policy and to obtain guarantees concerning the status and the conditions of student life. Yet, the students have a specific discursive strategy to attract the attention of government leaders and to increase their impact in the debates. During the strikes, DARUSO members generally refer to former university charters, which were established under the socialist regime. For instance, during a demonstration in 2007 the students stopped in front of the building of the central administration of the UDSM and pitched a song in honor to Nyerere's fight for the Independence of Tanganyika, the former Tanzania. By changing its lyrics, they used the song to stress their claim for an unlimited access to higher education (Ross 2008, p. 13). In January 2007, the student leaders asked for a better absorption of school fees by encouraging the government to learn from the education policies under J. K. Nyerere: "Students from all constituents of the University of Dar es Salaam had assembled at the Nkrumah Hall of the University of Dar es Salaam at 9.00 am yesterday from where they marched to Jangwani grounds. Once at Jangwani, they listened to their leaders, who said that the government should emulate Mwalimu Nyerere's example of prioritizing education for all eligible students, rather than making it a privilege of the well-to-do lots" (The Guardian 2007, p. 1). Striving for a consensus on their behalf, the students mobilize this reference to Mwalimu as a political argument in order to justify their claims. This reference is also used by the students in order to accuse the central administration and the presidential team for their corruptibility and the wasting of public funds. The students describe the numerous scandals about corruption as a consequence of the dilution of J. K. Nyerere's principles, like his integrity and honesty (Fouéré 2011, p. 82). For instance, in 2011 they wrote on their banners: "Mr. President: respect higher learning institutions, SH 5,000 is not enough" and "MPs and ministers' salaries and posh cars should be reduced to compensate students" [START_REF] Kagashe | UDSM students protest, clash with police in Dar[END_REF] success of the cost-sharing policy, which is reflected by the poor number of private students at the UDSM and the continuing involvement of the Tanzanian government in the funding of the sector, can be explained by the role of student mobilizations in the implementation of the reform. The socialist values of Nyerere, which have a broader political resonance in Tanzania, represent a discursive framework for students to renegotiate the implementation of public policy. However, the impact of the student's claims also depends on the adherence of government leaders to the socialist paradigm. In fact, the student's requests seem to fall on a breeding ground since the government replied positively to their demands. Government leaders are eager to maintain a pacific university and to avoid conflict situations, which could harm international funding. They even refer themselves to the memory of Nyerere's heritage. As a lecturer at the UDSM explained, most of the government leaders do not share the liberal ideology behind the cost-sharing policy: "You know, there is this thinking in Tanzania that everybody has the right to get [university] loans, has the right to access the loans. So they are not really associate very much with the philosophy behind the loans finance and the issues of cost-sharing and the issues of availability to pay. So, it is more a political issue rather than a technical issue"8 . Since the early 2000s, the official government discourses have been characterized by an extreme ambiguity shifting between the importance of privatizing higher education and the continuous validity of socialist principles. Several episodes of the reform processes underline this constant shifting between the liberal and the socialist paradigm. Paradoxically, in 2002 when the cost-sharing policy was in its final stage of implementation, the government strengthened its financial involvement in the higher education sector. On September 28th, Ruth Mollel, the Permanent secretary of the Ministry of Sciences, Technology and Higher Education expressed her concerns about a potential disengagement of the state from the sector: "I would like to allay fears expressed by many that by introducing cost-sharing in higher education the government intends to abdicate its responsibility as the main provider of this constitutional rights to every Tanzanian (…) Due to economic disparities, the Government recognized that not all students could raise enough funds for their studies" (Cosato 2002, p. 4). In May 2007, the Tanzanian president J. M. Kikwete then announced for instance, that students could no longer be excluded from the higher education institutions if they fail to pay their tuition fees due to the socio-economic conditions of their parents (Ross 2008, p. 11). In November 2008, the Minister of Education and Vocational Training, Jumanne Maghembe, also underlined the success of the Tanzanian system by comparing it to its neighbor countries: "Compared to Kenya and Uganda, Tanzania was far much ahead in providing more scholarships to students because 60,000 were being sponsored annually. Kenya sponsors 10,000 students while Uganda pays for only 4,000 students" (Mushi 2008, p. 3). In July 2009, Pius Ng'wandu, Minister of Science, Technology and Higher Education claimed for a further reduction of the registration fees (Mukiza 2009, p. 3). These hybrid discourses have been shaping the policy process of higher education. Whereas the succeeding governments have tended to foster the privatization of the UDSM since the early 1990s, the higher education policies however continue to confirm the prevailing frame of the socialist legacy of J. K. Nyerere in policymaking. We argue that these discursive and political shifts between liberalism and socialism in Tanzania present a variable to understand why the government is still funding an important number of students compared to other countries in the East African region. However, this ambiguity is not specific to the higher education sector, but can be observed in other policy fields in Tanzania, such as land reforms (author 2016; Schlimmer, 2017a 2017b). As explained by Michael Lofchie, Nyerere's ideology continues to shape Tanzanian policies and politics: "although Nyerere stepped down from the presidency nearly thirty years ago, and died fifteen years ago in October 1999, his social idealism continues to be a factor in Tanzania politics: it provides a counter-culture to the market system that currently prevails" (Lofchie 2014, p. 5). By using the image of Nyerere to renegotiate reforms [START_REF] Fouéré | Tanzanie: la à l'épreuve du post-socialisme[END_REF][START_REF] Fouéré | Julius Nyerere, Ujamaa, and political morality in contemporary Tanzania[END_REF], policy actors, like students in the case of higher education, reshape and reinvent its values and the memory of Tanzanian socialism. These configurations are a characteristic of postsocialist regimes, more generally, where memories of the past and situations of the present are intrinsically intertwined [START_REF] Landry | Post-socialisme, post-colonialisme et postérité de l'idéologie[END_REF][START_REF] Pitcher | African socialisms and postsocialisms[END_REF][START_REF] Fouéré | Tanzanie: la à l'épreuve du post-socialisme[END_REF][START_REF] Fouéré | Julius Nyerere, Ujamaa, and political morality in contemporary Tanzania[END_REF][START_REF] Fouéré | Remembering Julius Nyerere in Tanzania: History, legacy, memories. Dar es Salaam[END_REF].

The Tanzanian case has underlined the impact of domestic stakeholders on the negotiation of the implementation of higher education policies in a specific political and historical context. The aim of the second part is to test these variables in the case of the reforms at the University of Nairobi in Kenya.

The regulation of higher education reforms by the political elite and administrative stakeholders in a context of neopatrimonial politics: the case of the UoN in Kenya

Compared to Tanzania, where the cost-sharing policy is constantly renegotiated, the Kenyan case reveals another configuration with more private forms of higher education. Since the end of the 1980, several reforms have led to a progressive privatization of public universities, implying increasing private funds within the universities' budgets. This transition could be interpreted as the successful application of the interests and recommendations of international donor institutions and experts promoting the withdrawal of the state from the university sector. However, the following paragraphs will demonstrate that another explanatory variable is needed in order to perceive the implementation of the cost-sharing policies. We argue that the outcomes of the reforms strongly rely on the (political, professional, financial, etc.) interests, strategies and actions of members of the Kenyan political elite, as well as of the administrative and academic staff, disposing of an important reshaping power.

In contrast to the Tanzanian case, the Kenyan higher learning sector reflects fierce competition between different institutions and proliferating market initiatives [START_REF] Amutabi | Crisis and student protest in universities in Kenya: Examining the role of students in national leadership and the democratization process[END_REF]. Since the 1980s, there has been an increased development of public establishments not only in Nairobi, Kenya's capital, but also in the provinces and regions. For instance, the Moi University, which takes its name from the former President Daniel arap Moi, was established in 1984 in the West of the country in the Rift Valley. In 1987, the Egerton University (Nakuru) was inaugurated while the Jomo Kenyatta University of Agriculture and Technology opened its doors in 1994 (northeast of Nairobi). This growing university network was completed by the mushrooming of campus-satellites representing these public institutions on different territories all over the country. The multiplication of public higher learning institutions triggered a sharp competition for student enrollment. In fact, one of the particularities of the Kenyan sector is that the university market is shaped by rivalries between the public institutions striving for the attraction of "client students". From now on, students were considered as customers using the universities' services in return for tuition fees, which raise the institutional budget. This scramble for students has increased since the 1970s when private higher learning institutions were officially launched [START_REF] Eisemon | Private initiatives in higher education in Kenya[END_REF][START_REF] Oanda | University expansion and the challenges to social Development in Kenya: Dilemmas and pitfalls[END_REF]. In 2014, the country had twenty-two public universities and nineteen private institutions. The privatization of higher education has changed the role, the functions and the management of the public institutions. The most salient example of privatization is the University of Nairobi (UoN), the oldest and biggest public university of the country. A member the UoN stresses the confusion between the private and the public sectors by referring to a common joke among the staff of the UoN: "We are the oldest university in Kenya. We are the largest public university in Kenya. And we are also the largest private university in Kenya" 9 . An economic overview of the UoN budget underlines a tremendous financial growth in the university budget given the internally generated private funds. In fact, since 2007, the budget has mostly depended on private funds 10 .

Table 2: Evolution of the UoN budget, 2000UoN budget, -2011 The University of Nairobi Enterprises and Services (UNES), which was created in 1996, plays an important role in this privatization process and can be seen as "the private enterprising arm of the university" 11 . Its role and responsibility is to promote and to coordinate income-generating activities within the UoN, by identifying available resources and by commercializing activities. However, the tuition fees of private students represent the main part (around 80%) of the resources, which are generated by the UNES each year. Since 1998 students at the UoN have been divided into two groups depending on their source of funding. Students, which belong to "Module I" are funded by the government, whereas students of "Module II" are self-sponsored (e.g. financial 9 Interview conducted on November 30 th 2011, University of Nairobi, Nairobi. 10 This data is based on a review of the annual reports and accounts of the university including the years from 2000 to 2011 and on figures collected at the Budgetary control section at the UoN. support of relatives, personal salary, secondary jobs, access to a student credit from the Higher Education Loans Board) [START_REF] Marcucci | Higher educational cost-sharing, dual-track tuition fees and higher educational access: The East African experience[END_REF]. With the implementation of the Module II programs in 1998, the student enrollments have been soaring at the UoN: the total number of students has increased from 11,026 students in 1990 to 63,889 in 2012. Private students represented 70% of the total student enrollments in 2012 (author 2015, p. 466).

Several authors have interpreted these privatization processes within the Kenyan higher education system as a consequence of the increasing intervention of experts from the donor community, inspired by the liberal paradigm. For instance, Hélène Charton has argued that the implementation of the 1998 "Module II" program converges with the international education policies, which are promoted by the World Bank (Charton 2012, p. 239-240). A professor of the UoN confirmed this idea of internationally oriented policies: "Module was very much in keeping in the donor philosophy and for example the World Bank. This method is argued when they talk about higher education financing, and they argue that university has to implement tuition fees and that these tuitions fees have to be increased to increase university budget. And it was a part of the structural adjustment programmes. So, in that sense, Module II was linked with donors"12 . However, this argument must be scrutinized, given the ambiguous relations between Daniel Arap Moi's government and the donor community during the 1990s [START_REF] Ikiara | L'économie kenyane face à l'ajustement structurel[END_REF][START_REF] Brown | From demiurge to midwife: Changing donor roles in democratisation process[END_REF][START_REF] Campbell | International development and bilateral aid to Kenya in the 1990s[END_REF][START_REF] Colclough | A triumph of hope over reason? Aid accords and education policy in Kenya[END_REF]. The period was characterized by mutual skepticism, mistrust and several budget cuts, which impeded the collaboration on substantial policy reforms. The following paragraphs present further variables to question the predominant role of international experts as main entrepreneurs of privatization policies in the higher education sector. As in the Tanzanian case, the internationally promoted policy processes are constantly shaped by different groups of domestic actors who have specific interests in the transformation of the university sector, such as political elites, university managers and the teaching staff. The strategies and practices of these stakeholders must be analyzed against the background of neopatrimonal politics, which have characterized the Kenyan regime since Independence.

Political elites in Kenya have contributed to the formation of a privatized university system by spurring the competition between different public higher education institutions. State agents and politicians have built the higher education market as an instrument to legitimize their social positions through patron-client relations throughout the neopatrimonial regimes of Jomo Kenyatta (1964Kenyatta ( -1978)), Daniel arap Moi (1978-2002) and Mwai Kibaki (2002Kibaki ( -2013)). After the Independence in 1963, the establishment of ethnic-based patron-client relations became an essential tool for political elites who were striving for votes and political legitimacy [START_REF] Branch | Kenya. Between Hope and Despair, 1963-2011[END_REF][START_REF] Hornsby | Kenya. A history since Independence[END_REF].

Redistributive politics has marked policy processes in various fields, and especially the higher education reforms [START_REF] Munene | Our university: Ethnicity, higher education and the quest for state legitimacy in Kenya[END_REF]. During the 1970-1980s, the Moi government followed a two-fold purpose by establishing new public universities: firstly, by decreasing the public budget for the UoN, Moi tried to reduce the power of an institution, which had close ties to members of the political opposition, and especially to the former President Jomo Kenyatta. Secondly, the government aimed to create and to distribute political and academic offices, by creating new public institutions, such as "his" Moi University in Eldoret. Thus, despite the ongoing economic crisis during the 1970s and 1980s, Moi continued to mobilize public resources in order to expand the higher education sector. A report of the Kenya Human Rights Commission underlines Moi's enthusiasm to invest in his newly established university: "to control the UON further, new strategies were instituted. These included the shifting of resources from the UON to other universities, and particularly to the Moi University in Eldoret. Lecturers and professors were offered higher salaries at Moi University and many left for Eldoret (Kenya Human Rights Commission 1992, p. 5)". Financial resources were shifted from the former leading university in Nairobi to the youngest institution in Eldoret, in order to gain political support from within the university community. This example demonstrates how political elites use the building of universities as a tool to win votes and political legitimacy, by mobilizing public funds for individual political interests (Klopp and Orina 2002, p. 53). These neopatrimonial practices, which blur the borders between private and public spheres, are often linked with redistributive politics based on ethnical criteria. During his government between 1964 and 1978, J. Kenyatta used the establishment of new public universities as a patronage resource. He was the first president to build a university, through the recruitment of administrative and academic staff and student enrollments, with the aim to woo specific ethnical groups and exclude others. In fact, the Kenyan president and its government seek to control the UoN in order to strengthen the hegemony of the Kikuyu13 . D. a. Moi's regime performed the same practice with the Moi University by balancing the power towards the minority groups (the ethnic alliance KAMATUSA between the Kalenjin, Masaai, Turkhana and Samburu) (Munene 2012, p.8-10). This strategy of vote buying is also pursued through the disjunction of former satellite campuses and their transformation into new public universities. In 2001, D. a. Moi converted the Maseno College in the district of Kisumu, which was part of the Moi University, into a full-fledged university (Maseno University), to secure the Luo vote in the 2002 general elections (Munene 2012, p.12). By creating new public universities out of existing colleges, the government divided the "national cake". Considerable amounts of public resources were allocated to the district of Kisumu in order to hire administrative and academic staff, to provide scholarships for students and to construct new buildings and infrastructures. The president M. Kibaki undertook similar strategies, with the aim to increase his political legitimacy. Under his regime, new public universities and campus-satellites were established to consolidate patronage practices and to obtain political support to remain in power 14 . During 2012 and 2013, Kibaki's government chartered 15 new public universities and thereby disrupted the higher education sector [START_REF] Mburu | Government set to charter 15 public varsities[END_REF][START_REF] Nyasato | Kibaki charters 13 th public university. The Standard[END_REF].

This mushrooming of public universities implicitly triggered sharp competition between the different institutions, especially regarding student enrollment. The emerging higher education market continued to grow through the progressive privatization of public universities since the end of the 1990s. This transition towards privatized higher education is encouraged and accelerated by policy decisions and professional interests of administrative staff within the universities. The most speaking case is the privatization of the UoN. At the end of the 1990s, the heads of the public universities were confronted with different administrative challenges: firstly, they had to deal with the effects of the redistribution of public resources among the newly established higher education institutions. Secondly, the demand for access to tertiary education was increasing in a context of democratization. Thirdly, Kenya faced a financial crisis, which affected the economy and diminished public investment in several policy sectors. In order to tackle these financial challenges, the UoN administrative staff decided to diversify the university's revenue sources, by promoting new income-generating activities. These economic strategies were heavily recommended by the government, which asked the university's managers to change their financial policies [START_REF] Kiamba | Privately sponsored students and other income-generating activities at the University of Nairobi[END_REF]). In fact, the latter transformed this context of uncertainty into an opportunity to make the UoN more self-reliant and competitive regarding other higher learning institutions.

This process of internal restructuring was led by the Vice-Chancellor (1991-2002) and professor of civil engineering, F. J. Gichaga. 15 In order to implement these administrative changes, F. J. Gichaga created a team composed of close colleagues 16 with links to economic networks and banks, who were in favor of liberal policies and the privatization of the higher education system. In 1994, F. J. Gichaga's team set up an "Income generating committee" (University of Nairobi, 1994), chaired by F. Kibera, a professor in management and who later became a member of the Board of the Central Bank of Kenya. The committee's mission was to evaluate the university's income-generating activities and to formulate recommendations. C. Kiamba, a UoN Vice-Chancellor (2002-2004) and future Permanent Secretary of the Ministry for higher education, science and technology (2006)(2007)(2008)(2009)(2010)(2011)(2012) explained the key role of F. J. Gichaga and his hybrid team in the restructuring of the university's economy: "The committee introduced the concept of the entrepreneurial university, adding a business model to the conventional mission of the university. To achieve this end, the committee stressed the need to identify university resources and their commercial exploitation […]. The committee further noted increasing evidence to show that any university, given its reservoir of expertise in the development and transmission of knowledge could become adaptive and entrepreneurial simply through an innovative use of the existing conventional structures, but with appropriate change in delivery systems, personnel, and organizational structures" (Kiamba 2004, p. 56). By introducing a business model into the missions of public service, the UoN was transformed into an "entrepreneurial university". The aim was to identify potential economic resources of the university and to define the conditions to exploit them commercially. The University Council approved the recommendations of the committee and the UNES was registered in May 1996 to centralize the funds generated by these income-generating activities. The UNES is charged of inculcating entrepreneurial and business practices into each department of UoN. These new economic activities encompass the development of consultancy, the commercialization of university products (bookstores, restaurants) and services (the clinical chemistry, the diagnostic radiology, the veterinary farm and even a funeral parlor). The implementation of these economic tools has gradually expanded the decision-making power and the legitimacy of university managers by increasing their financial autonomy vis-à-vis the Ministry of higher education.

However, the members of F. J. Gichaga's team were also driven by personal interests. As members of the academic staff of the UoN, they joined other lecturers and professors requesting an increase of their salaries. During the financial crisis of the 1980-1990s the lecturers' and professors' monthly wage decreased from 500 US$ in 1985 to 230 US$ in 1994(The Economic Review 1994, p. 12). At the beginning of the 1990s and in a context of democratization, protests of academic staff advocating for their rights and requesting higher salaries17 were increasing [START_REF] Adar | Human rights and academic freedom in Kenya's public universities: The case of the universities academic staff union[END_REF]author 2015a, p. 492-503). According to F. J. Gichaga, the different aspects of the privatization process formed a response to these claims. The integration of private students and the diversified incoming-generating activities helped to increase the salaries. Given the limited capacity of the government to improve the welfare of the academic staff significantly, the economic reforms of the university managers were the only option to respond positively to the professors' claims (Gichaga 2011, p. 123). Through the internal restructuring of the UoN during the 1990s and 2000s, the salaries increased considerably and today, the established income-generating activities cover even more than 40% of the wages18 . However, since the 2000's, new strikes have been organized by members of the teaching staff, claiming a better sharing of the university's private incomes and further pay raises [START_REF] Maupeu | Les réformes néolibérales des universités est-africaines: Eléments d'analyses à partir du cas kényan[END_REF]author 2015a, p. 496-503). In 2011, when groups of academics came together for a new series of demonstrations challenging the UoN's internal policies, a lecturer explained his colleagues' complaints regarding the partition of the private funds: "We work a lot but we are not paid appropriately. The money goes anywhere in the university As these empirical examples demonstrate, the privatization of the UoN serves the interests of the universities managers aiming for a liberalized and entrepreneurial higher education system on the one hand and the academic staff requesting an increase of their salaries on the other hand. This convergence of interests can be explained by the double position of most of the reform leaders, who belong both to the administrative and the academic staff. These dynamics within the UoN complete the patron-client practices of politicians who use higher education policies to foster neopatriomonial politics. Both variables are necessary in order to understand the driving processes of the privatization of the UoN and the increasing competition between higher learning institutions in the country, more generally.

Conclusion

This article discusses the multi-stakeholder dynamics driving higher education reforms during the 1990s and 2000s in Tanzania and Kenya. Whereas most scholars have focused on the influence of international stakeholders on university reform processes, such as donors and experts, this article adds an original variable for studying the negotiation of these policies. In fact, the presented case studies reveal the determining role of domestic actors in the higher education sector. National and local stakeholders negotiate and regulate policy processes, even in states whose incomes partially depend on external aid. Even if international stakeholders contribute to the reforms, especially during policy formulation, these policy processes do not represent vertical top-down mechanisms. Both in Tanzania and in Kenya, higher education reforms are nationally and locally (re)negotiated. However, when it comes to analyzing these domestic reform processes, the case studies reveal several differences, which lead to varying policy outcomes. In fact, the implementation of cost-sharing policies, which are supposed to lead to a greater involvement of private funding and to a financial discharge of the government, does not entail privatization in both countries. In Tanzania, where the extent of the implementation of the cost-sharing policy remains limited since the 2000s, student claims and mobilizations turn out to play a major role. Their success in the negotiations is based on discursive strategies reminding the memories of the role of higher education in the formation of a socialist nation. This systematic use of Nyerere's heritage reflects a political culture, oscillating between socialism and liberalism and characterizing the postsocialist regime since the 2000s. Tanzanian politics are still shaped by an in-between space, which regulates policies of which the higher education sector is a prime example. In the Kenyan case, where privatization reforms have been more successful, the policy process is framed by members of the political elite, university managers and the teaching staff. Neopatrimonial politics and ethnically oriented patron-client relations have stimulated a proliferation of public universities and an increasing competition between these institutions. This emerging competition is reinforced by the strategies of university managers who strengthen their influence vis-à-vis the Ministry of Higher Education by creating income-generating activities. We have completed this analysis by underlining the determining role of lecturers and professors in the implementation of the reforms. In fact, the growing private funds flowing into the university's budgets lead to a steady increase of the salaries of the academic staff and turn them into the clear winners of the entrepreneurial reforms. Altogether, the comparison between the Tanzanian and Kenyan stresses the key role of national and local actors in higher education policy process.

The Tanzanian and Kenyan reform processes provide different results regarding the privatization processes. Whereas the UoN's budget has been largely privatized since 2007, the Tanzanian government remains the principal sponsor of the UDSM. In Kenya, the UNES has developed new entrepreneurial and business activities within the establishment. Since 1998, the biggest part of these private incomes has been generated through the implementation of the Module II programs, promoting the enrollment of private students, which currently represent more than 70% of the total enrollments at the UoN. In Tanzania, the extent of the privatization process remains limited. Despite the implementation of the cost-sharing policy in 1992, the registration rate of private students at the UDSM remains a contrario relatively low. Although the cost-sharing reform was introduced to encourage the withdrawal of the state from the higher education sector, the government still prevails as the main funder of the public universities. Thus, Kenya and Tanzania feature two opposing models of higher education systems: one is highly commercialized and conducted by the privatization of public universities; the other is predominantly financed by public funds despite the implementation of liberal policies.

These results underline the scientific interest to multiply and to compare monographic case studies on policy making in Sub-Saharan Africa. However, as both case studies demonstrate the negotiation of the design and of the implementation of policies must be combined with a closer analysis of the political regime and its historical path. Policy analysts have progressively disconnected their approaches from the broader studies of historical state making. Yet, the features of the political regime affect policy processes, by influencing the practices of stakeholders who negotiate and implement reforms.

  [laughter] […]. I think with these [private] students the university earns four billions each year [in Kenyan shillings]. But one does not know where the money goes" 19 .

  . The limited 7 Interview conducted on March 12 th 2010, University of Dar es Salaam, Dar es Salaam.

		A: Budget allocated by the government (US $)	B: Budget allocated by external donors (US $)	Total budget (A+B) (US $)	Percentage of public funds in the UDSM budget (%)
	1998	11,493,644	9,841,612	21,335,256	54
	1999	12,427,426	9,436,231	21,863,657	57
	2000	16,754,605	11,610,608	28,365,213	59
	2001	13,935,690	10,794,282	24,729,972	56
	2002	15,391,693	11,035,822	26,427,515	58
	2003	16,689,547	9,185,060	25,874,607	65
	2004	16,544,895	9,168,721	25,713,616	64
	2005	25,823,511	-	-	-
	2006	26,468,208	-	-	-
	2007	28,128,372	5,728,150	33,856,522	83
	2008	45,919,193	11,791,576	57,710,769	80
	2009	39,893,296	28,608,598	68,501,894	58

  11 Interview conducted on November 8 th 2011, University of Nairobi, Nairobi.

		A: Budget allocated by the government (US $)	B: Budget generated by private activities (US $)	Total (A+B) (US $)	Percentage of the private funds in the global budget (%)
	2000	19,793,303	9,252,194	29,045,497	32
	2001	20,748,796	17,163,563	37,912,359	45
	2002	20,787,437	18,131,830	38,919,267	47
	2003	21,911,787	26,836,417	48,748,204	55
	2004	25,003,673	26,644,710	51,648,383	52
	2005	35,553,823	35,204,113	70,757,936	50
	2006	50,983,813	42,028,995	93,012,808	45
	2007	48,963,964	49,272,450	98,236,414	50
	2008	50,569,652	52,211,082	102,780,734	51
	2009	46,827,016	58,064,315	104,891,331	55
	2010	47,066,847	68,517,438	115,584,285	59
	2011	52,235,429	72,508,268	124,743,697	58

Interview with a manager of the Committee of the university research, administrative and financial reforms of the Makerere University, conducted on May 10th

2012, Makerere University, Kampala (Uganda).

Interview conducted on March 17 th 2010, University of Dar es Salaam, Dar es Salaam.

Interviews conducted on February 22 th 2010 and March 3 rd 2010, University of Dar es Salaam, Dar es Salaam.

Interviews conducted on May 13 th 2013, Dar es Salaam.

This term refers to students who pay their tuition fees through the financial support of their relatives or their personal incomes. The official definition of "private students" used by the government does not include those students who obtain loans from the Higher Education Students' Loan board (HESLB). This definition reflects that the government and the academic staff seek to uphold an image of publicly funded students. However, even if students on loans were considered as "private students", this category would need to be questioned, since only few students pay back their loans. The Tanzanian loan system is not completely efficient, (Provini 2015a, p. 534-545), since the board has no viable loan recovery mechanism(Bailey et al. 2010, p. 21) inciting students not to pay back the allocations once they have finished their studies. According to a high member of the academic staff, this dysfunctioning of the HESLB is founded on political motivations, rather than on purely technical reasons. The absence of a sanctioning mechanism for students who fail to recover their loan, is based on the political conviction, that the financing of higher education costs must be the responsibility of the government: « the higher education loans board is kind of a political organisation. It is unfortunate. I say it is unfortunate because funding education should be done politically correct because these are people for your country. This is the most important resource for your country. It should not be a propaganda issue ». Interview conducted on April 30 th 2013, University of Dar es Salaam, Dar es Salaam.

Interview with an administrative staff member conducted on April 30 th 2013, University of Dar es Salaam, Dar es Salaam.

Interview conducted on May 14th 2013, University of Dar es Salaam, Dar es Salaam.

Interview conducted on January 16 th 2012, University of Nairobi, Nairobi.

Interviews conducted with a professor on November 1 st and December 2 nd 2011, University of Nairobi, Nairobi.

At that time, a lecturer explained the academic staff's discontent and requests: "Lecturers in Kenya are grossly underpaid. They earn a pittance compared to their colleagues in other universities, where dons earn six digit figures. Here, we are on the floor of a five digit figure. The universities' medical scheme has literally collapsed, while high rents are forcing us out of premises leased by the universities. Academic staff have no housing scheme and universities are increasingly failing to pay rents" (The Economic Review 1994, p. 12).

Interviews conducted with a professor on November 1 st and December 2 nd 2011, University of Nairobi, Nairobi.

Interview conducted on November 30 st

2011, University of Nairobi, Nairobi.