Towards Extending the Ethical Dimension of Human Resource Management
Bibliographic record
Abstract
Enduring interest in the ‘social’ aspect of the ethical dimension of Human Resource Management (HRM) on employees and society is a positive trend towards humanity. To maintain justice, fairness and well-being towards its stakeholders, it is necessary for an organization to perform HRM functions ethically. Authors identified two possible meanings to the ethical dimension of HRM. In addition to the above, a second possible connotation was recognized, and labeled as ‘Ethical Orientation of HRM or EOHRM’. This is ‘to direct HRM functions to create, enhance and maintain ethicality within employees, to make an ethical workforce in the organization’. EOHRM is conceptualized based on three dimensions: acquire, develop and retain. Elements of EOHRM are the functions of these three HRM fields. Ethical characteristics would be embedded into elements and question items of the instrument, in order to measure EOHRM. It seems that this concept has been unexplored by scholars in the existent HRM literature. This article attempts to bridge this knowledge gap to a significant extent. EOHRM is offered as a novel concept to HRM architecture, and it gives favorable directions towards future research.
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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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".