Typologie des conceptions des universités en vue d'en évaluer la performance: Rendre compte de la diversité pour en saisir la complexité
Bibliographic record
Abstract
The university system is complex and is constantly pressured to evaluate its performance. How should university performance be defined? There is no agreement on the dimensions, criteria and indicators to choose. This article presents a typology of the conceptions of universities so as to evaluate their performance. Based on an extensive literature review, a typology prototype consisting of seven conceptions of universities was developed. A method of anasynthesis (Silvern, 1972; Sauvé, 1992; and Legendre, 2005) was used to verify the typology. Semi-structured interviews were held with eleven experts. These experts assessed the proposed typology in relation to six validation criteria (clarity, logic consistency, comprehensiveness, economy, usefulness and acceptability by users). This article presents the results of this research as well as the seven optimal typology categories (public service, market, academic, learner, political, entrepreneurial and living environment).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".