Funding policy and funders’ role in driving academic-practitioner collaborations
Bibliographic record
Abstract
This report highlights the key ideas and findings of a mapping exercise conducted in the initial phases of the ‘Funding Policy and Funders’ (FPF) sub-project, which developed out of the 'Exploring the Potential of Academic-Practitioner Collaborations for Social Change (AcPrac)1 project hosted under the LSE’s AFSEE programme. The AcPrac project has two key objectives: 1) to contribute to AFSEE’s theory of change by exploring the conditions that are conducive to developing generative processes of knowledge exchange between academics and practitioners; and 2) to examine the methodological and epistemological challenges of researching inequalities, and particularly how the latter might be reproduced through the research process itself. The FPF sub-project investigates how the funding landscape shapes and drives AcPrac collaborations for social change, focusing on funding programmes that broadly address the reduction of inequalities.
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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 | 0.193 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.021 | 0.025 |
| Scholarly communication | 0.053 | 0.023 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".