Pratiques responsables des dirigeants de PME : influence du profil du dirigeant
Bibliographic record
Abstract
The challenges faced by SME owner-managers regarding Corporate Social Responsibility (CSR) are increasing. Not only do SMEs represent an important part of the global economy, they also have major environmental, social, and local impacts within the communities in which they are embedded. In addition, the sustainable practices implemented in SMEs are often initiated by their owner-managers. Consequently, the impact of SME owner-managers? characteristics on the development of sustainable practices needs to be investigated. Few prior studies look at SME owner-managers? impact on their firms? sustainable involvement, and most assess their intentions rather than the practices put in place. To fill this gap, an empirical study conducted in France and Canada with 212 SME owner-managers focused on the one hand on their intrinsic characteristics, such as age and gender, and on the other on their extrinsic characteristics, such as education level and personal actions taken to increase environmental awareness. Results show that whilst the gender of the SME owner-manager does not have an impact on their firms? sustainable practices, education and mainly owner-managers? personal involvement in environmental protection have a significant impact on these practices. In some contexts, the influence of age can be explained by the owner-managers? experience.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".