Universalism, Ascription and Academic Rank: Canadian Professors, 1987–2000*
Bibliographic record
Abstract
L'auteur se penche sur le classement par rang des professeurs canadiens en s'appuyant sur deux enquêtes réalisées en 1987 et en 2000. Il émet cinq hypothèses: a) les publications, l'expérience et l'obtention d'un Ph.D. sont les principaux éléments influençant le classement par rang; b) les hommes blancs et ceux qui sont nés au Canada sont avantagés dans le classement par rang; c) les femmes et les membres des minorités visibles ont été moins désavantagés au cours des derniéres années; d) l'interaction de l'avancement et de l'attribution favorise les Blancs, les hommes et les personnes qui sont nées au Canada, mais dans une moindre mesure pour les deux premiers groupes depuis quelques annees; et e) finalement, de telles interactions avantagent les professeurs enseignant les sciences naturelles plus que les autres disciplines. Les résultats appuient (a) et (b), mais sont mitigés concernant (c) et (d). Ils ne corroborent pas (e). This paper examines rank placement of Canadian professors based on two surveys conducted in 1987 and 2000. Five hypotheses are pursued: a) publication, experience and possession of Ph.D. are the main ingredients of placement in ranks; b) males, Whites and those born in Canada are advantaged in rank placement; c) women and visible minorities have been less disadvantaged in recent years; d) the interaction of achievement and ascription favours Whites, males and those born in Canada, but less so for the first two groups in recent years; and finally e) such interactions favour professors in the natural sciences more than in other disciplines. Results support (a) and (b) but are mixed regarding (c) and (d). They do not support (e).
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".