Mesure de la productivite multifactorielle a Statistique Canada
Bibliographic record
Abstract
Le present document decrit l'evolution du Programme de la productivite multifactorielle lance a Statistique Canada en 1987, ainsi que les ameliorations apportees a la mesure de la productivite multifactorielle depuis ce temps. Ces ameliorations prennent en compte les nouvelles notions abordees dans les ouvrages economiques publies, les meilleures sources de donnees disponibles et les besoins de la collectivite des utilisateurs. Le document resume en outre les recherches effectuees a partir d'autres donnees et methodologies pour evaluer l'exactitude du Programme de la productivite multifactorielle et pour eclairer des domaines que les programmes internationaux courants sur la productivite multifactorielle laissent de cote. Enfin, le document fait etat des orientations pour l'avenir qui sont envisagees en vue d'ameliorer encore la mesure de la productivite a Statistique Canada.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".