How qualitative criteria can improve the assessment process of interdisciplinary research proposals
Bibliographic record
Abstract
Abstract Despite the increasing recognition for the scientific and societal potential of interdisciplinary research, selection committees struggle with the evaluation of interdisciplinary proposals. Interdisciplinary proposals include a wider range of theories and methods, involve a more diverse team, pose a higher level of uncertainty, and their evaluation requires expertise from multiple disciplines. In this study, we investigate the possibility to support the evaluation of interdisciplinary research proposals with measures of interdisciplinary research quality. Based on the literature, we curated a set of qualitative criteria and bibliometric indicators. Subsequently, we examined their feasibility using interviews with interdisciplinary researchers and a re-assessment session of a grant-allocation procedure. In the re-assessment session members of an original evaluation panel assessed four original research proposals again, but now supported with our measures. This study confirmed the potential of qualitative criteria to assess the interdisciplinarity or research proposals. These indicators helped to make explicit what different people mean with interdisciplinary research, which improved the quality of the discussions and decision-making. The utility of bibliometric indicators turned out to be limited, due to technical limitations and concerns about unintended side effects.
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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 | 0.511 | 0.667 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.017 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".