The use of indicators in French Universities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of indicators in the management of French universities is becoming more and more prevalent and advanced, at least as far as HSS (humanities and social sciences) are concerned. In the present chapter, we will provide evidence of the general use of indicators and of differences between disciplinary fields. In order to put these results in context, we will first provide some information on the French system and how the recent reforms favored the development of indicators. We will then describe what we have learnt from the qualitative study on the attitudes of the humanities and the sciences to indicators. We will then present some lessons drawn from a quantitative study in which we were able to compare universities mainly specialized in humanities with universities mainly specialized in the sciences. In doing so, we will start out by looking at the use of indicators. This issue has been largely studied in the management sciences, and different authors have suggested different uses. Simons for instance distinguished between diagnostic use of indicators (indicators are used to produce an evaluation of performance) and interactive use of indicators (indicators are used to reveal strengths and weaknesses and to learn about them). Cavalluzzo and Ittner also distinguish between reporting (i.e. providing information about activities), and steering or making decisions (using indicators in order to introduce change). Drawing on these two typologies, we first look at cases where indicators are used to legitimize what has been done and to account for it. Indicators are produced in order to show that a level of performance is achieved, to provide data required by external actors, describing current achievements. We will also consider cases where data are produced in order to compare units or teams and thus to evaluate their activity. Finally, we look at cases where data and indicators are used in order to make decisions or choices and to take action. The legitimation, evaluation, discussion and decision uses of indicators will be studied for data on teaching, on research and on budgets in order to see whether different issues lead to different uses. A second issue addressed by the present chapter deals with disciplinary differences. In France, there are some 'complete universities' (with or without medicine), but also many universities specialized in law and economic sciences, universities with a strong orientation in the NS (natural sciences), and universities that are specialized in the HSS. This allows us to compare the uses of indicators in the humanities and the science‑dominated institutions (HSS institutions and NS institutions) in the following: the former represent approximately 15% of the French universities and the latter 14%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it