SOCIAL ORGANIZATIONAL RESPONSIBILITY MANAGEMENT MODELS: WHAT LESSONS FOR HUMAN RESOURCES MANAGEMENT?
Bibliographic record
Abstract
This communication represents a work in progress by the first author, who is enrolled in a research program of master in organizations management. The notion of social corporate responsibility (SCR) is increasingly present in management literature. It is linked to profitability, investor attraction and brand image. More recent research has begun to investigate the link that can be made between SCR and human resources management (HRM). In the vein of this field of research, our paper proposes to take a closer look at how certain SCR models, implemented consciously or not by certain companies, could impact the employee perception of employment relationship quality. The concept of “quality of life at work” and social identity theory will be used to measure the employment relationship quality. To carry out the research, a qualitative methodological approach, based on comparative case study, will be used. A sample of about 20 participants (manager and employees) will be targeted. The results of the research could provide a better understanding of how the decisions taken in relation to SCR could be combined to form original models of social responsibility management. Moreover, the study of the links that can be established between SCR models and the perception of the employment relationship quality could allow companies to better manage their human resources and improve retention and attraction of the 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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".