ETHICAL HRM PRACTICES IN INDIA: A CHALLENGE
Bibliographic record
Abstract
Ethical challenges abound in HRM. Each day, in the course of executing and communicating HR decisions, managers have the potential to change, shape, redirect, and fundamentally alter the course of other people's lives. Managers make hiring decisions that reward selected applicants with salaries, benefits, knowledge, and skills, but leave the remaining applicants bereft of these opportunities and advantages. Managers make promotion decisions that reward selected employees with raises, status, and responsibility, leaving other employees wondering about their future and their potential. Managers make firing and lay-off decisions in order to improve corporate performance, all the while harming the targeted individuals and even undermining the commitment and energy of the survivors. Even when managers complete performance appraisals and deliver performance feedback, they may inspire one employee and devastate another. For each HR practice, there are winners and there are losers: those who get the job, or receive a portfolio of benefits, and those who do not. This paper explores and determines the standards which is very important to complete the entire task ethically and make a justified decision with each individual weather employee or candidate.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".