Préférences des PME en matière de politiques publiques de responsabilité sociale des entreprises : une comparaison France-Québec1
Bibliographic record
Abstract
Quelles approches en matière de politiques publiques sont préférées par les PME pour soutenir leurs actions de responsabilité sociale ? Ces préférences diffèrent-elles selon les pays ? Une enquête auprès de 300 PME en France et au Québec montre qu’elles préfèrent les approches accompagnatrices aux approches coercitives. Les PME des deux régions réagissent de façon assez semblable à la majorité des politiques qui leur sont présentées, quoique les PME québécoises semblent mieux accepter les approches coercitives et sont plus sensibles aux incitatifs financiers que les PME françaises. Ces différences sont analysées sous l’angle des particularités culturelles des pays.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".