Bibliographic record
Abstract
Following 20 years of publishing rank and reputation scores for Canada’s 49 institutions of higher education, the present analysis tested five hypotheses: (1) rank and reputation should be positively correlated across schools for each year; (2) rank and reputation should be positively correlated across the 20 years for each school; (3) a school’s rank variance should be equivalent to a school’s reputation variance; (4) previous reputation would predict current rank; and (5) previous rank would predict current reputation. Results showed that whereas rank corresponded roughly to reputation for a given school, there are noteworthy exceptions. One in seven schools offered a reliable correlation between rank and reputation, and four school correlations were negative. Rank and reputation variability correlated, though (marginally) better-ranked schools had stable reputation scores over the years. Implications for future ranking exercises are discussed, as are directions for future research. Voilà maintenant 20 ans que l’on publie le classement et la réputation des 49 établissements d’enseignement supérieur canadiens. Dans cet article, nous examinons cinq hypothèses : 1) le classement et la réputation devraient faire l’objet d’une corrélation positive parmi les établissements d’année en année; 2) le classement et la réputation devraient faire l’objet d’une corrélation positive sur toute la période de 20 ans pour chaque établissement; 3) les variations dans le classement d’un établissement devraient se refléter dans les variations de la réputation; 4) l’indice de réputation antérieur permettrait de prévoir le classement actuel d’un établissement; et 5) le classement antérieur permettrait de prévoir l’indice de réputation actuel d’un établissement. Selon les résultats de notre analyse, bien que le classement et la réputation puissent être équivalents dans le cas d’un établissement en particulier, il existe des exceptions notoires. Dans le cas d’un établissement sur sept, il y a une corrélation sûre entre le classement et la réputation. Dans le cas de quatre établissements, la corrélation s’est montrée négative. Il y a une corrélation entre les variations du classement et de la réputation, quoique les établissements un peu mieux classés ont des indices de réputation plus stables au fil du temps. Nous présentons enfin les conséquences de ces résultats pour la conception de futurs palmarès et nous proposons des avenues pour la recherche.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".