Employees Perception on Gender diversity on banking performance in Kathmandu valley
Bibliographic record
Abstract
Studying gender diversity in firm performance has been the subject of research request for more than three decades now. Yet, in context of Nepal, study related to the issue is still not abundant. Therefore, this study aims to study the present status of gender diversity on banking performance in Nepalese context that includes several benefits, challenges associated with it and policies and measures that need to be taken to promote gender diversity. The study adopted descriptive cross section research design with the survey questionnaire technique. With the help of convenient sampling technique 300 bank employees were selected purposively from all 27 A grade commercial banks residing Kathmandu valley. The study results that 100% respondents working in commercial banks in Kathmandu valley were aware about gender diversity and most of them have been aware gender diversity through their family and social media. Interestingly, this study also found that employees are not facing any challenges regarding gender diversity signifying that Nepalese commercial Banks have been practicing gender diversity friendly working environment. Similarly, building an inclusive workplace could be one of the best managerial solutions for maintaining and enhancing gender diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".