Policy Transfer Research Articles

Instrument constituencies and the brokering of OECD-knowledge in Nordic school reforms - a three country comparison

ABSTRACT Major international surveys and reports have considerably altered the expectations and outlooks of national policymakers in education over the last three decades. Through a comparative content analysis of bibliographies in policy documents, this article explores the intermediary bodies that facilitate salient interconnections between international organizations such as the Organization for Economic Cooperation and Development (OECD) and national policymakers and experts who prepare comprehensive school reforms in three Nordic countries: Denmark, Norway and Sweden. This article builds on a large Nordic research project that studied the transfer and translation of international policies in a Nordic reform context. It extends the study by examining the role of publishers as intermediary policy brokers that legitimize the usage of OECD knowledge as significant members of instrument constituencies. This study enhances our understanding of the variations in citation patterns across policy documents from three Nordic countries. It also investigates the extent to which differences in instrumental constituencies arise from institutional arrangements and the ideational foundations underpinning reform trajectories. Additionally, it examines the topics addressed by these reforms and the governments’ affiliations with new policy brokers supported by both public and private publishers.

As a Public Diplomacy Component Technology Management Policy and Technology Transfer Offices of Turkey

This paper explores the role of Turkey's National Technology Management Policy and Technology Transfer Offices (TTOs) as components of public diplomacy. The National Technology Management Policy plays a significant role in shaping Turkey's international image, influence, and soft power. The centralized approach to policy formulation and implementation underscores the strategic alignment of technology management policies with the country's objectives, including those related to public diplomacy. Additionally, TTOs play a crucial role in facilitating the transfer of technologies and knowledge between universities, research institutions, and industry, contributing to Turkey's technological advancement and competitiveness. The paper evaluates the impact of these components on Turkey's public diplomacy efforts and their potential to enhance the country's international standing and influence.

Politico-Administrative Culture and Public Service Reform in Post-Independence Kazakhstan

Classical organizational management literature draws clear parallels between organizational culture and climate and effective use of power and influence as key to successful policy implementation of reforms in public sector organizations. On the other hand, the public policy literature, in particular, policy transfer as a strand within policy studies, emphasizes the role of the national context, more specifically, ‘facilitators’ and ‘constraints’ of ‘‘politico-administrative culture” within the national context, as crucial to understanding processes of transfer, convergence, and diffusion of public policy. There is a plethora of studies by Western scholars of public management who have successfully utilized these theoretical underpinnings to study the effectiveness of public service reforms in mature policy environments such as the UK, the US, Australia, New Zealand, and others. However, the public policy and comparative public management literature only offers a limited number of case studies from developing, middle-/upper-middle countries, which rely on concepts of organizational management in addition to narratives on the impact of policy learning from global doctrines, such as Weberianism, New Public Management (NPM), and New Public Governance (NPG), and national politics, on the implementation of administrative reforms in those contexts. Kazakhstan, as a resource-affluent post-Soviet country and a bastion of modernization and ‘open government’ in Central Asia or the Commonwealth of Independent States (CIS) in the post-Soviet era is a case in point. Based on ethnographic research consisting of interviews with elite academics, civil servants, and think-tank activists, as well as reviews of OECD and government strategy reports in Astana, the findings point to a potential abatement of the impact of context constraints such as large power distance and collectivist behavior by context facilitators such as those surrounding the use of ‘trilingualism’ and public diplomacy towards reforms in Kazakhstan particularly in recent years.

Efficient Bayesian Policy Reuse With a Scalable Observation Model in Deep Reinforcement Learning.

Bayesian policy reuse (BPR) is a general policy transfer framework for selecting a source policy from an offline library by inferring the task belief based on some observation signals and a trained observation model. In this article, we propose an improved BPR method to achieve more efficient policy transfer in deep reinforcement learning (DRL). First, most BPR algorithms use the episodic return as the observation signal that contains limited information and cannot be obtained until the end of an episode. Instead, we employ the state transition sample, which is informative and instantaneous, as the observation signal for faster and more accurate task inference. Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal. Hence, we propose a scalable observation model based on fitting state transition functions of source tasks from only a small number of samples, which can generalize to any signals observed in the target task. Moreover, we extend the offline-mode BPR to the continual learning setting by expanding the scalable observation model in a plug-and-play fashion, which can avoid negative transfer when faced with new unknown tasks. Experimental results show that our method can consistently facilitate faster and more efficient policy transfer.

From Policy Advisors to Transfer Agents: Understanding the Role of Policy Advisors in the Circulation of Good Governance Principles in the Global South

By the moment policy advisory system (PAS) gains field as a scientific object in public policy; different studies on the subject explore its importance in political systems. They reveal that PAS provides a significant support to public policy decision for central and local governments, thereby highlighting its impact in decision and policy-making process within these systems. While agreeing with this evidence, the present paper focuses on the role of Policy Advice Actors (PAA) in the diffusion and transfer of good governance principles at the international level. The aim of this paper is therefore to contribute to the debate on the multiple facets of policy advisory system in Africa by analyzing the dynamics of policy advisors in policy decision. It eventually tries to link PAS to policy diffusion and highlights how the PAS is being used by these actors to crystallize their knowledge and ideas into different political systems. The present paper is a qualitative analysis based on empirical evidence and the existing literature on the issue in Africa and particularly in Cameroon. Henceforth, it is argued that policy transfer especially diffusion is an important mechanism of PAS in the global south, of course as external policy advice is concerned, international organizations that constitute the main actors of this system are engaged into providing global solution for southern countries rather than policy innovation. Despite this evidence these actors may somehow be confronted to different degrees of complexity into the local context.

State Marine Aquaculture Policy Dashboard improves transparency and accessibility for growing industry.

Marine aquaculture (mariculture), the farming of marine species, is currently a relatively small contributor to the United States' seafood industry. There is tremendous potential for growth in this sector and increasing interest in advancing the industry through supportive federal and state policies as well as concerns about how to best manage potential negative impacts or unsustainable development. While some mariculture is conducted in land-based tanks as well as saltwater ponds, and there are pilot projects and ongoing applications to commence mariculture operations in federal waters, the majority of mariculture activities occur within state waters (typically, 3 nautical miles, or 5.5 kilometers, from shore) and are largely managed by state-level policies and regulations. The policy mechanisms by which each of the 23 coastal states manages their respective mariculture industries are quite varied, making it difficult to identify policy trends and assess which approaches may be enabling or impeding the development of the sector. As such, we present the State Marine Aquaculture Policy Dashboard: a publicly-available, living database collating state-level policy and legislative data related to the management of the mariculture industry. This centralized, accessible catalog of laws, policies, regulations, and initiatives is a valuable resource for understanding the current landscape of state mariculture policy frameworks in the U.S. and can create opportunities for policy transfers and collaboration across states as they seek to manage their industries.

The adoption of dual Vocational Education and Training in Spain: Analysis of descentralised decision-making in educational policy transfer

This paper examines the adoption of dual VET in Spain as a process of educational policy transfer. Using Phillips and Ochs’ (2003, 2004) policy transfer spectrum and decision-making classification and Perry and Tor’s (2008) model of transfer forms, the study relies on semi-structured interviews with key decision-makers in the Spanish central government and the Autonomous Communities of the Basque Country and Andalusia. Findings indicate that EU diffusion activities facilitated adoption, involving first-order learning. Initially, adoption at the central level was uninformed, driven by quick-fix decisions, but later shifted to informed transfer through second- and third-order learning, enhancing realism. In Andalusia and the Basque Country, decision-making consistently adapted to the real context, progressing from second- to third-order learning. Ultimately, this analysis concludes that dual VET adoption at the central, Andalusian, and Basque levels falls between ‘negotiated under constraint’ and ‘borrowed purposefully’, though not at the same point.

Energy-Efficient and Accelerated Resource Allocation in O-RAN Slicing Using Deep Reinforcement Learning and Transfer Learning

Abstract Next Generation Wireless Networks (NGWNs) have two main components: Network Slicing and Open Radio Access Networks (O-RAN). NS is needed to handle various Quality of Services (QoS). O-RAN adopts an open environment for network vendors and Mobile Network Operators (MNOs). In recent years, Deep Reinforcement Learning (DRL) approaches have been proposed to solve some key issues in NGWNs. The primary obstacles preventing the DRL deployment are being slowly converged and unstable. Additionally, these algorithms have enormous carbon emissions that negatively impact climate change. This paper tackles the dynamic allocation problem of O-RAN radio resources for better QoS, faster convergence, stability, lower energy and power consumption, and reduced carbon emissions. Firstly, we develop an agent with a newly designed latency-based reward function and a top-k filtration mechanism for actions. Then, we propose a policy Transfer Learning approach to accelerate agent convergence. We compared our model to another two models.

Comparing two transfer spaces over time and against a global script: the case of school-autonomy-with-accountability

ABSTRACT The study compares the selective adoption of the global school-autonomy-with-accountability (SAWA) reform in two cantons of Switzerland (Zurich and St. Gallen) over the period 1995–2015. The authors dissect the SAWA reform into three aspects: its theory of change, its bundle of policies, and its preferred policy instruments to achieve and sustain change. Methodologically, the study draws on a database of over 1,200 policy-relevant documents and expert interviews related to the so-called ‘outcomes-oriented school reform’ (German: wirkungsorientierte Schulreform) or SAWA, respectively. In an endeavor to advance research on policy borrowing, the authors propose a distinction between six temporal features of policy transfer: time, timing, tempo, lifespan, age, and sequence. They find that SAWA’s neoliberal theory of change was not adopted. However, select policies and instruments were transferred and translated into the Swiss context. Furthermore, some policies expanded over time (standardized testing), some were dropped after a while (performance pay), some were kept on the books but, with age, hollowed out over time (external evaluation), and some were long-lived and were institutionalized (professional school management).

A transfer learning model for cognitive electronic reconnaissance of unmanned aerial vehicle: Experiments

Applying Deep Reinforcement Learning (DRL) technologies to Unmanned Aerial Vehicle (UAV) electronic reconnaissance is one of the current research hotspots. However, simulation and engineering practice show that due to poor generalization of DRL models, the performance of Cognitive Electronic Reconnaissance (CER) policies based on training will significantly decrease when the mission scene undergoes slight changes. To address this issue, we thoughtfully combine the mission area segmentation technique with transfer DRL and propose a difference-adaptive transfer DRL algorithm. This algorithm involves mission subarea segmentation, subarea pre-training, multi-subarea policy transfer, and multi-subarea splicing, providing an efficient solution to the convergence problem of the DRL algorithm caused by mission space expansion and reward sparsity. Additionally, a general CER transfer learning simulator is developed based on the analysis of the capabilities of the maneuvering platform and electronic reconnaissance payload. Multiple sets of CER policy transfer learning experiments are designed for different mission spaces, mission difficulties, and UAV characteristics. Compared with the algorithm baseline, our designed policy model significantly outperforms: the mission completion rate of UAVs in multi-scale mission spaces improves by up to 37.4%, reaching 97.5%, while the training time is reduced by 2.46 h. Further behavior analysis shows that this policy model enables UAVs to exhibit target tracking behaviors such as hovering and sustained approach.

Missions, conditions and the policy transfer of Smart Specialisation in the European Union

ABSTRACT Smart Specialisation is fundamentally a bottom-up policymaking process premised on entrepreneurial discovery and peer learning. Nonetheless, the European Union has relied on coercive policy transfer through the ex-ante funding conditionality instrument to secure its implementation across regions. This paper examines how the use of conditionality has shaped the implementation of Smart Specialisation in the European Union and its regions. Furthermore, it discusses whether the use of conditionality to ensure the implementation of a particular type of innovation policy reflects a changed perception of the role of the state in the broader European integration project.

Advocacy Coalitions and Education Policy Transfer: Lessons from School Board of Trustees Policy in Georgia

The article explores the relationship between education policy change and policy transfer, focusing on Georgia's School Board of Trustees. It analyzes how Western practices have been implemented in the education policies of post-Soviet countries. To achieve this, the article utilizes instrumental case studies and in-depth interviews. It views policy transfer as a tool of education policy change while integrating it with an advocacy coalition framework. The study indicates that advocacy coalitions transform into transfer networks, and their approach is to replicate or emulate foreign experiences. The article also poses new research questions, enabling researchers to build on and scrutinize the proposed assumptions and concentrate on post-Soviet education policy transformation.

The school-autonomy-with-accountability reform in Iceland: looking back and making sense

The study examines the selective adoption of the school-autonomy-with-accountability (SAWA) reform at upper secondary school level in Iceland. It draws on a review of policy documents (acts, amendments to acts, regulations) and interviews with 17 individuals, including four former ministers of education that served over the period 1995-2017. The authors identify three distinct reform waves: a general public management reform (NPM) implemented across all sectors (1991 – 1995), the school autonomy reform (1995-2009), and the school-autonomy-with-soft-accountability reform (2009-2015). The sequence explains why opening up to the private sector during NPM required all line ministries to come up with clearly defined tasks and expected outcomes and why the third wave was an attempt to (minimally) reregulate the sector. The proliferation of standardized testing, a feature of SAWA in many other countries, did not occur in Iceland. Strikingly, the argument for autonomy was from the onset linked to diversity, that is, enabling upper secondary schools to respond to students’ diverse needs and interests and thereby reduce student drop. The study attempts to demonstrate the importance of the temporal dimension of policy transfer such as, for example, the timing, tempo, sequence and duration of adopted policies.

Policy Transfer for Sexual Violence Prevention and Management in Indonesian Higher Education Institutions

This research delves into the intricacies of policy transfer in the domain of sexual violence prevention and management within Indonesian Higher Education Institutions (HEIs). Through a phenomenological approach, the study engaged with 15 key informants and scrutinized 17 policy documents, uncovering that the applicability of such policies is not universal and is significantly influenced by diverse social, economic, and political contexts. The study underscores the important role of effective communication with the Ministry of Education, Culture, Research, and Technology, advocating for an understanding of the varied contexts in which these policies are executed. It highlights the disconnection between policy formulation and its practical enactment, suggesting that initial attempts at linear policy transfer eventually evolve into more complex processes once implementation unfolds. This complexity arises from the intersection of theoretical policy frameworks with the pragmatic realities of institutional contexts, underlining the need for adaptable and context-sensitive approaches in the fight against sexual violence in educational settings.

Selective policy transfer in multi-agent systems with sparse interactions

Previously trained single-agent strategies, which are considerably easier to acquire than multi-agent strategies, are instructive for multi-agent reinforcement learning, especially when the interactions between agents are sparse. Traditional methods of knowledge transfer, from single-agent source tasks to multi-agent target tasks, typically rely on a pre-designed Markov-decision-process similarity function or metric function for evaluating the task similarity. In this study, we propose a selective policy transfer (SPOT) algorithm that eliminates the need for the manual crafting of a metric function to assess the similarity between source and target tasks. The SPOT algorithm enables agents to autonomously determine when and which policy to transfer using well-trained single-agent policies as options in the training process. We introduced a multi-agent policy-learning option in the option library, thus allowing the SPOT algorithm to leverage the transferred knowledge while concurrently learning new policies. The SPOT algorithm efficiently transfers sequential strategies in a few steps, thereby capturing high-level semantics. Experimental results obtained in both multi-agent arcade and multi-agent particle environments have demonstrated that the proposed algorithm outperforms the state-of-the-art methods in terms of jump-start and convergence speeds. Our evaluation indicators included the jump start, steps to threshold, cumulative reward, and asymptotic performance. Furthermore, visualizations of the strategies and termination functions of the agents aid in elucidating the operational principles of the SPOT algorithm.

Deep Reinforcement Learning for sim-to-real policy transfer of VTOL-UAVs offshore docking operations

This paper proposes a novel Reinforcement Learning (RL) approach for sim-to-real policy transfer of Vertical Take-Off and Landing Unmanned Aerial Vehicle (VTOL-UAV). The proposed approach is designed for VTOL-UAV landing on offshore docking stations in maritime operations. VTOL-UAVs in maritime operations encounter limitations in their operational range, primarily stemming from constraints imposed by their battery capacity. The concept of autonomous landing on a charging platform presents an intriguing prospect for mitigating these limitations by facilitating battery charging and data transfer. However, current Deep Reinforcement Learning (DRL) methods exhibit drawbacks, including lengthy training times, and modest success rates. In this paper, we tackle these concerns comprehensively by decomposing the landing procedure into a sequence of more manageable but analogous tasks in terms of an approach phase and a landing phase. The proposed architecture utilizes a model-based control scheme for the approach phase, where the VTOL-UAV is approaching the offshore docking station. In the Landing phase, DRL agents were trained offline to learn the optimal policy to dock on the offshore station. The Joint North Sea Wave Project (JONSWAP) spectrum model has been employed to create a wave model for each episode, enhancing policy generalization for sim2real transfer. A set of DRL algorithms have been tested through numerical simulations including value-based agents and policy-based agents such as Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) respectively. The numerical experiments show that the PPO agent can learn complicated and efficient policies to land in uncertain environments, which in turn enhances the likelihood of successful sim-to-real transfer.

Impact evaluation with process tracing: explaining causal processes in an EU-interreg sustainable tourism intervention

Drawing from the field of complex evaluations we discuss a novel application of process tracing for the evaluation of complex tourism interventions. We argue that to better evidence impact of tourism interventions and facilitate policy transfer we ought to adopt approaches to evaluation that allow us to deepen our understanding of causal mechanisms at play in an intervention. We adopt process tracing as a qualitative, theory-based evaluation method to make within-case causal inferences about impact. To showcase the method, we apply it to evaluate the outcomes of a real-world EU-Interreg sustainable tourism intervention called “EXPERIENCE,” implemented across six pilot regions across France and England. We argue that deepening our understanding of how interventions work in a local context is necessary for the design and transferability of future interventions across similar contexts.

Examining primary healthcare services following earthquake shocks in Turkiye: A critical analysis of the initial 3months and the vital role of humanitarian policy transfer for rapid response.

The purpose of this article is to discuss the importance of policy transfer by humanitarian NGOs to post-disaster regions and the effectiveness of mobile Primary Health Care (PHC) services immediately after disasters. This study also focused on analysing the first 3months aftermath of the earthquake and assessed the changes in the access and needs of vulnerable groups in emergency response creation and systematic interventions after disasters. In disasters that require urgent response such as earthquakes, the importance of the existing NGOs (Such as MdM) capacity in the countries has emerged in terms of rapid response and experience sharing. During the field work, it is observed that MdM Mobile Medical Units (MMU) teams had played a key role in terms of tracking PHC needs, functioning as early warning system for epidemics, and prevention of communicable diseases in the EQ effected areas. In this aspect, the changes in diagnoses in the first trimester is examined using the comparative analysis methods. This study used a cross-sectional mixed-method approach in terms of objective evaluation. The results from a quantitative needs assessment were complemented by qualitative data. Herein, the data were collected in two stages: First, primary data was collected through the field activities of MMU, and second field-based assessments was conducted with health professionals who have been working in the MMU teams since the first moments of the earthquake.

Policy transfer and the movement of Chinese river chief system

ABSTRACT Much has been written about policy learning and transfer over the past 20-years. However little of this has examined the role of transfer within authoritarian nations. We propose to start addressing this by examining the movement of the river chief system from its original setting in the city of Wuxi in the Jiangsu Province to Suqian, Nantong, Suzhou and Huzhou, in Zhejiang Province. To do this we looked at two basic patterns of transfer: (1) horizontal transfer from one municipality (or combination of several) to another, and (2) vertical transfer up to and down from the Provincial level. This allowed local agents to act either autonomously in the transfer and learning processes or act under some level of compulsion to engage in the act of transfer. Based on our four case studies we argue that even when transfer involves a high degree of autonomy and translation of the transferred model to the local circumstances, successful outcomes are not guaranteed, just as when agents are forced into transfer a given model to their jurisdiction, there is no guarantee that it will result in policy failure.

External auditors, policy transfer, accruals accounting and the United Nations

PurposeThis study asks whether external auditors enable the transfer of policies to the United Nations organizations that they audit and, if so, what types of policies are transferred.Design/methodology/approachThe empirical research is based on a content analysis of 512 external auditor recommendations from 28 pre- and post-accrual reports of 14 UN bodies.FindingsWe find that external auditors do enable policy transfer and that such involvements may, at times, veer into non-neutral policy spaces.Research limitations/implicationsWe did not analyze all UN organizations with accruals-based accounting. We also did not engage in a longer longitudinal study.Practical implicationsOur findings raise new questions about international organization accountability, the technocratic and policy-specific influences of external auditors, and open a debate about whether attempted policy transfers can be neutral.Originality/valueThe world’s largest group of international organizations is affiliated with the UN. External auditors help ensure that member-state monies are appropriately utilized. Our study is the first to compare pre- and post-accrual external auditor recommendations for 14 UN bodies. It is also the first to notate and study the attempted policy transfers from external auditors to the audited UN bodies.