Changing Governance of Public Research: Research-for-Development (R4D) Funders in the United Kingdom, Canada and Australia
Bibliographic record
Abstract
Research-for-development (R4D) has been viewed as generating evidence on the effectiveness of foreign aid, inventing new technologies that serve poor people, and strengthening research capabilities in poor countries.What determines which of these policy goals are pursued?This thesis argues that public funders continually adjust their approach based on pressure to satisfy the expectations of their home governments.The concepts of performance regime and program theory are used to examine how funders responded to changing expectations and maintained credibility with both their government sponsors and the research community.The United Kingdom, Canada, and Australia all experienced a shift towards market-inspired governance of public research, which favoured shorter time-horizons and encouraged funders' to alter their perspective regarding whom to target for funding (researchers at home and abroad) and what constituted the point of delivery (between field and lab-based science).Funders resisted to a limited extent by adapting selectively to these pressures, inserting their own ideas into the performance regime, and covertly pursuing their existing program theory.Yet performance regimes matter.Changes in performance expectations clearly led to fundamental changes in program theory and grantmaking practice.In previous decades, funders had some degree of freedom, to interpret broad policy directions and mediate between public research and international development, drawing on the ideas championed by the individuals that founded and led each organization.Yet performance Chapter: Setting the SceneResearch-for-development (R4D) concerns research directed towards the benefit of developing countries, including the strengthening of research capabilities within these countries.A number of organizations fund R4D including private foundations, bilateral donors and specialized agencies.1 The label of 'research funder' distinguishes these organizations from those that perform, regulate, and use research.They provide a bridge between the source of their funding, and the research communities and host societies that are the intended beneficiaries of their activities.A key challenge facing these organizations is how to allocate limited funding among competing priorities in a way that satisfies multiple stakeholders so as to maintain the support from their home government and credibility with researchers and host societies.This challenge is further complicated as R4D funders bridge policies for international development and science: research can directly address the needs of poor women and men, yet the capability to absorb, adapt and develop knowledge also benefits society at large.The OECD's Development Assistance Committee fosters a model of aid effectiveness that encourages harmonization among donor agencies, alignment with the priorities of host countries, and focus on particular countries and sectors.Meanwhile, both advanced and developing countries adopt domestic science policies that identify thematic priorities perceived to benefit their societies.Over time, R4D funders must 1 Examples include bilateral donors (e.g.Swedish International Development Agency and the Netherlands' Ministry of Foreign Affairs), specialized agencies (e.g. the World Bank and France's Institut de recherche pour le développement) and private philanthropies (e.g.Rockefeller Foundation and the Bill & Melinda Gates Foundation).The thesis consists of seven chapters.This first chapter introduces the research question, hypothesis, and identifies key ideas from the literature on policies governing publiclyfunded research.Chapter two provides the theoretical framework which draws on the concepts of context (or "performance regime") surrounding a research funding organization, and its agency ("program theory") in choosing among competing goals and responding to various expectations.Chapter two also describes the data sources used and methods of analysis employed in the case studies that follow.Chapters three, four, and five recount the historical experience of three R4D funding organizations: the United Kingdom's Department for International Development (DFID) 2 , Canada's International Development Research Centre (IDRC), and the Australian Centre for International Agricultural Research (ACIAR).Chapter six provides a synthesis which compares the three case studies to the theoretical framework.All three countries experienced a shift towards market-inspired governance, which favoured a shorter-term research agenda.R4D funders partially adapted to this context while trying to influence it and continued to pursue their program theory covertly.Chapter seven identifies some implications for the future of R4D and the research community, as well as the contribution this thesis makes to the literature.This introductory chapter presents a brief overview of 'research-for-development' (R4D)and presents the problem statement and hypotheses.The key question is how do funders decide on their approach to R4D, and why do these approaches vary over time and Research QuestionWhy do funders periodically change how they define and fund research? HypothesisThey respond to a performance regime …selectively as an adaptive organization
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".