Measuring bias, burden and conservatism in research funding processes
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> Grant funding allocation is a complex process that in most cases relies on peer review. A recent study identified a number of challenges associated with the use of peer review in the evaluation of grant proposals. Three important issues identified were bias, burden, and conservatism, and the work concluded that further experimentation and measurement is needed to assess the performance of funding processes. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> We have conducted a review of international practice in the evaluation and improvement of grant funding processes in relation to bias, burden and conservatism, based on a rapid evidence assessment and interviews with research funding agencies. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> The evidence gathered suggests that efforts so far to measure these characteristics systematically by funders have been limited. However, there are some examples of measures and approaches which could be developed and more widely applied. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> The majority of the literature focuses primarily on the application and assessment process, whereas burden, bias and conservatism can emerge as challenges at many wider stages in the development and implementation of a grant funding scheme. In response to this we set out a wider conceptualisation of the ways in which this could emerge across the funding process. </ns4:p>
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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 | 0.798 | 0.879 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".