Bibliographic record
Abstract
This paper on women in labour unions in India highlights the occupational segregation suffered by women in union structures. The authors explore and document the extent of female participation in trade unions in India. They suggest that less than 8 per cent of the 380 million workforce in India are unionized and women account for a very small fraction of trade union membership. They provide a number of reasons for the low female membership and participation in unions. In the occupations where women are organized, the incidence of union leadership among women varies considerably. On the positive side, the authors note that India has been a pioneer in organizing women in the informal sector such as workers’ cooperatives, self help groups such as Working Women’s Forum and Self Employed Women’s Association etc. In fact, they find that these unions are creating social unionism, thereby rewriting the meaning of trade unionism. The focus is on broad objectives of empowerment, development and fighting for their rights rather than the business unionism in North America (that is, focus on the bread and butter issues alone). The initiatives dictated by the Indian Constitution such as reservations or quotas for scheduled castes and scheduled tribes may have to be applied to labour unions and the private sector employers in the case of women in India. Policy makers and managers can learn a great deal from the theories discussed above.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".