Bibliographic record
Abstract
Le document presente des estimations de la croissance effective de la productivite multifactorielle (PMF) au Canada, aux Etats-Unis, en Australie, au Japon et dans certains pays de l?Union europeenne (UE), fondees sur la base de donnees EU KLEMS sur la productivite et sur les tableaux mondiaux des entrees-sorties. La croissance effective de la PMF permet de determiner l?incidence des gains de productivite dans les industries en amont sur la croissance de la productivite et la competitivite internationale des industries nationales, ce qui procure une mesure pertinente de la croissance de la productivite et de la competitivite internationale de la production de produits de demande finale, comme les produits de consommation, de placement et d?exportation. Une part importante de la croissance de la PMF, particulierement dans les petites economies ouvertes comme celle du Canada, est attribuable aux gains de la production d?intrants intermediaires dans les pays etrangers. La croissance de la productivite tend a etre plus elevee pour les produits de placement et d?exportation que pour les produits de consommation. Les progres techniques et la croissance de la productivite dans les pays etrangers ont davantage contribue a la croissance de la production de produits de placement et d?exportation qu?a celle de produits de consommation. L?analyse presente des donnees empiriques coherentes avec l?hypothese que la croissance effective de la PMF constitue un indicateur plus approprie de la competitivite internationale que la croissance standard de la PMF.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".