Bibliographic record
Abstract
Il a toujours été problématique de conceptualiser les professions puisque les caractéristiques professionnelles varient beaucoup dans le temps et dans l'espace. Cet article analyse les façons selon lesquelles les professions et les occupations ont été délimitées historiquement dans la législation. Un regard sur la réglementation professionnelle de cinq provinces canadiennes antérieure à 1961 dénombre environ 36 groupes professionnels distincts. L'auteure croit qu'une meilleure compréhension s'appuyant sur la recherche empirique de ce qu'étaient les professions par le passé aidera à conceptualiser et à faire avancer la recherche actuelle sur les professions. Particulièrement importantes sont les considérations sur la qualité de même que sur la structure des relations d'emploi avec les autres groupes professionnels, le public et l'État. Conceptualizing professions has traditionally been problematic because professional characteristics vary across time and place. This paper explores the ways in which professions and occupations were historically demarcated through legislation. A look at professional regulation in five Canadian provinces before 1961 reveals approximately 36 distinct professional groups. I argue that developing a more accurate, empirically based understanding of what professions were in the past will help us conceptualize and advance research on professions in the present. Particularly salient are considerations of status, as well as the structuring of occupations' relations with other occupational groups, the public, and the state.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.011 | 0.079 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".