Diversity in India: addressing caste, disability and gender
Bibliographic record
Abstract
Purpose The purpose of this paper is to present the diversity and equality perspectives from the national context of India and introduce a special issue about equality, diversity and inclusion (EDI) in India. Design/methodology/approach This special issue consists of six articles on current EDI issues in India. The first three of the contributions are focused on descriptions of diversity challenges and policies regarding caste and disabilities, while the remaining three papers address gender diversity. Findings In addition to providing an overview of this issue's articles, this paper highlights developments and current themes in India's country-specific equality and diversity scholarship. Drawing on the special issue's six papers, the authors show the relevance of Western theories while also pointing to the need for reformulation of others in the context of India. Research limitations/implications The authors conclude with a call to further explore diversity in India and to develop locally relevant, culture-sensitive theoretical frameworks. Religious and economic diversity should receive more attention in future diversity management scholarship in the Indian context. Originality/value How does India experience equality and diversity concepts? How are India's approaches similar or different from those experienced in other countries? How do theoretical frameworks originated in the West apply in India? Are new, locally grounded frameworks needed to better capture the developments at play? These questions are addressed by the contributions to this special issue.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".