Bibliographic record
Abstract
When science is evaluated by bureaucrats and administrators, it is usually done by quantified performance metrics, for the purpose of economic productivity. Olof Hallonsten criticizes both the means (quantification) and purpose (economization) of such external evaluation. I share the concern that such neoliberal performance metrics are shallow, over-simplified and inaccurate, but differ in how best to oppose this reductionism. Hallonsten proposes to replace quantitative performance metrics with qualitative in-depth evaluation of science, which would keep evaluation internal to scientific communities. I argue that such qualitative internal evaluation will not be enough to challenge current external evaluation since it does little to counteract neoliberal politics, and fails to provide the accountability that science owes the public. To assure that the many worthy purposes of science (i.e. truth, democracy, well-being, justice) are valued and pursued, I argue science needs more and more diverse external evaluation. The diversification of science evaluation can go in many directions: towards both quantified performance metrics and qualitative internal assessments and beyond economic productivity to value science’s broader societal contributions. In addition to administrators and public servants, science evaluators must also include diverse counterpublics of scientists: civil society, journalists, interested lay public and scientists themselves. More diverse external evaluation is perhaps no more accurate than neoliberal quantified metrics, but by valuing the myriad contributions of science and the diversity of its producers and users, it is hopefully less partial and perhaps more just.
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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 | 0.265 | 0.390 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.038 | 0.060 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.013 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".