Transforming excellence? From ‘matter of fact’ to ‘matter of concern’ in research funding organizations
Bibliographic record
Abstract
Excellence is omnipresent in the research ecosystem but the narrow focus on excellence is increasingly controversial. One of the key actors in the research ecosystems are research funding organizations, yet their activities are comparatively little studied in relation to excellence. This paper aims to contribute to the excellence debate through an empirical study of how notions of excellence are used, and what functions they serve, in eight research funding organizations. Our study shows research funding organizations have recognized critical voices and are taking steps to address some of the problematic aspects of excellence. However, because research funding organizations are shaped by the excellence regime, and constrained by both governmental policy and scientific elites, research funding organizations cannot simply resort to a debunking critique and do away with excellence altogether. In their efforts to navigate their ambiguous relationship to excellence we show that in many of our case study sites the approach to excellence has shifted from it being taken as a ‘matter of fact’, that is rather taken for granted, to a ‘matter of concern’, that needs to be unpacked and reconfigured. We find funders resort to three mitigation strategies, patching, pluralizing and transforming, in their attempts to reconfigure excellence. Our findings suggest, however, that the current mitigation strategies adopted by funders aimed at reconfiguring excellence leave underlying assumptions about competition and meritocratic ideals largely unquestioned. A transformation of the research ecosystem is unlikely to happen when these ideals are not also problematized.
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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 | 0.106 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.014 | 0.068 |
| Scholarly communication | 0.034 | 0.030 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".