Règlements salariaux selon la jurisdiction, l'industrie basé sur le système de classification des industrie de l'Amérique du Nord (SCIAN) et l'indice à la vie chère (IVC), Emploi et Développement social Canada - Programme du travail, mensuel
Bibliographic record
Abstract
Ce tableau contient 7560 séries, avec des données pour les années 1977 - 2017 (il n'y a pas nécessairement de données pour toutes les années pour l'ensemble des combinaisons). Ce tableau contient des données telles que décrites par les dimensions suivantes (Les combinaisons ne sont pas toutes disponibles) : Géographie (14 éléments : Canada; Sphère de compétence fédérale; Terre-Neuve-et-Labrador; Île-de-Prince Édouard; ...) ; Industrie (12 éléments : Toutes les industries; Industries primaires; Services publics; Construction; ...) ; Secteur (3 éléments : Tous les secteurs; Publique; Privé) ; Indice à la vie chère (IVC) (3 éléments : Toutes les règlements salariaux; Avec l'indice à la vie chère (IVC); Sans l'indice à la vie chère (IVC)) ; Variable (5 éléments : Nombre de convention collective; Nombre d'employés; Durée des conventions en mois (moyenne); Rajustement de la première année en pourcentage (moyenne); ...).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".