A CROSS LOOK AT THE ORGANIZATION'S COMMITMENT TO SOCIAL RESPONSIBILITY. CASE OF A MINING COMPANY IN QUEBEC, CANADA
Bibliographic record
Abstract
This paper presents research results from a larger project, focusing on the study of the links between employees' perceptions of their social identification and the social responsibility management models implemented by their organizations.In this article, we will present the subject of comparative analysis from the perspective of the company and its employees, in relation to the social responsibility management model implemented by the company.The study area for this research is represented by a company in the field of gold mining in the province of Quebec, Canada.Thus, using an exploratory qualitative research method based on the case study, we were able to observe firstly that the integrative model that we designed represents a relevant tool for the analysis of CSR.What's more, the research results showed similarities between the vision of company managers and its employees on CSR, but also divergences, which leads us to conclude that additional efforts should be made by organizations in order to improve their management of social responsibility and make sure it is well known by its employees.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".