Perspectives of HRM Professionals on Diversity, Equity, and Inclusion in Nepal
Bibliographic record
Abstract
Diversity Management has not yet been a significant part of now much discussed topic HRM in Nepal although the concepts of Diversity Management (DM) emerged in the USA, Canada in 1980s. Most of the developed countries in European Union have been the flagship of diversity management. There is handful of renowned organizations today either catering HRM services or implementing full-fledged HRM discipline in their own organizations. College text books and, seminars and dialogues conducted by development sectors have indicated that the emergence of concepts of Diversity, Equity and Inclusion (DEI) is pertinent in today’s context. In developed countries, both profit making and non-profit making organizations have been emphasizing the concepts of DEI and focusing on those in organizations. It’s a thought[1]provoking idea to learn the perspectives of current organization leaders in Nepal about their views on DEI. The article has been prepared based on findings of in-depth interviews taken with leaders and/or senior HR managers of reputed companies those are promoting HRM in Nepal. It attempts to provide an exploratory review seeking to shed light on their perspectives of regarding DEI in their pre-established HRM domain. The article explores the opinions, experiences and competencies and the initiatives taken by HRM leaders. The article also discusses the challenges faced by the HRM leaders in the pursuit of promoting the diversity management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".