Human resources management, sustainable development, and social responsibility
Bibliographic record
Abstract
The article examines the theoretical and empirical links between human resources management and social responsibility/sustainable development. This exercise illustrates how an approach based on social responsibility/sustainable development can contribute to the renewal of policies and practices in human resources management. The empirical results demonstrate how French and Quebecois business leaders view the integration of social responsibility/sustainable development into the policies and practices of human resources management. Reconciling the concept of economic efficiency with social and environmental principles within the social responsibility/sustainable development model represents a considerable challenge for professionals in human resources management. French leaders see the link between social responsibilities/sustainable development and human resources management as based on three concerns: economic efficiency, environmental consciousness, and social equity. Quebecois leaders see the connection between human resources management and social responsibility/sustainable development as reflected in a company’s obligations toward the well-being of its workers. French and Quebecois leaders point out that human resources should put greater emphasis on: (1) applying deontological and ethical principles within the organization, and (2) setting up programs that can ensure that the values and principles of social responsibility/sustainable development are disseminated throughout the organization. The article concludes that human resources management must not only be concerned with enhancing organizational performance, but must also factor in environmental and social issues when formulating corporate strategy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".