Ranking Research: Toward an Ethnostatistical Perspective on Performance Metrics in Higher Education
Bibliographic record
Abstract
Cet article prend une perspective ethnostatistique sur l’utilisation d’indicateurs de performance dans l’enseignement et la recherche en management. De récents développements dans la littérature en sciences sociales montrent que les chiffres peuvent être un dispositif de contrôle sur les organisations. Toutefois, cette littérature omet d’explorer comment les chiffres sont construits, comment un sens leur est attribué et ce qui permet leur contrôle sur les organisations. L’ethnostatistique est un moyen de combler ce manque en explorant la construction et l’utilisation de mesures dans les organisations. Trois niveaux d’analyse sont examinés. Un agenda de recherche pour l’étude scientifique des indicateurs de performance pour la recherche en management est décrit. Cet article suggère qu’une intervention basée sur SEAM peut utiliser l’ethnostatistique pour découvrir les coûts cachés des indicateurs de performance, et donc de développer des indicateurs améliorés.
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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 | 0.074 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".