Formes de propriete et niveau d'efficacite technique des entreprises canadiennes
Bibliographic record
Abstract
Abstract This paper seeks to further examine the ownership-performance relationship and to overcome some methodological dificulties encountered hitherto in this field of research. By and large, the results show a real or relative decline of privatized State-owned enterprises y (SOE) technical eficiency in the post-privatization period. While some analyses show no signijcant difference between the eficiency of SOEs and that of their private counterparts, others, on the contrary, suggest that SOEs are more eficient. As for our study, it came to the conclusion that SOEs have never seemed to be less efficient than private companies. Résumé La prénte étude vise à examiner plus en profondeur la relation entre la forme de propriété et la performance et à pallier certaines difficultés méthodologiques rencontrées jusqu'alors dans ce domaine de recherche. Dans l'ensemble, les résultats indiquent une baisse réelle ou relative de l'efficacité technique des entreprises privatisées suite au transfert de leur propriété. Si certaines analyses comparant l'efficacité entre les sociétés d'Etat et les entreprises privées du mȩme secteur démontrent l'absence de différence entre les deux groupes, d'autres, au contraire, suggerent que les sociétés d'Etat sont plus efficaces. Notre étude démontre quant à elle que jamais les entreprises publiques ne sont globalement apparues comme moins efficaces par rapport aux entreprises privées.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".