Bibliographic record
Abstract
Purpose This paper aims to present the challenges facing women in India due to the intersectionality of gender and other forms of identities impacting on their personal and professional lives by exploring the intersection of gender, colour, caste, ethnicity, religion, marital status, and class as sources of discrimination against women in Indian society and workplaces. Design/methodology/approach The approach is discussing the socio‐cultural traditions leading up to the complexities of multiple intersections of identity for women living and working in India, offering a paradigm shift from Western issues of gender equality towards understanding women's empowerment issues within the Indian context. Findings Indian women are marginalized in their access to education and healthcare, and they are also compromised in their personal and professional development by being undervalued, underemployed and under‐rewarded. The social implications are the impact of awareness, changing attitudes and corporate social responsibility interventions towards improving the quality of life of women in India. Multinational corporations as well as Indian organizations may be influenced to implement diversity policies and practices beyond individual identities to incorporate the complex intersectionality that is the reality and dilemma of the challenges faced by Indian women in society, in professional careers and within organizations. Originality/value Readers will find originality and value in understanding the complexities of gender equality issues in India as compared to other countries and contexts. It can inform researchers, academics, practitioners and policy makers on how to address the disparities and discrimination against women and guide comparative discourses between India and other countries towards eliminating discrimination against women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".