Qu'est-ce que la productivite? Comment la mesure-t-on? Quelle a ete la productivite du Canada pour la periode de 1961 a 2012?
Bibliographic record
Abstract
Le present document fournit un apercu du programme de productivite de Statistique Canada et une breve description du rendement du Canada en matiere de productivite. Il definit la productivite et les diverses mesures utilisees pour examiner les differentes facettes de la croissance de la productivite. Il decrit la difference entre des mesures de productivite partielles (par exemple, la productivite du travail) et une mesure plus complete (productivite multifactorielle) ainsi que les avantages et desavantages de chacune de ces mesures. Le document explique pourquoi la productivite est importante. Il decrit sommairement comment la croissance de la productivite s?integre dans le cadre comptable de la croissance et comment on utilise ce dernier pour examiner les diverses sources de croissance economique. Il presente brievement les defis que les statisticiens doivent relever lorsqu?ils mesurent la croissance de la productivite. Il fournit egalement un survol de la productivite a long terme du Canada et compare celle-ci a celle des Etats-Unis, selon les niveaux de productivite et selon les taux de croissance de la productivite.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".