Women’s empowerment and micro-entrepreneurship in India: Constructing a new development paradigm?
Bibliographic record
Abstract
While the contribution of women to the economies of developing countries is critical, women rarely find employment in the regulated unionized sectors of these countries, and are found instead in overwhelming numbers in the sector that is variously termed ‘unorganized’, ‘unprotected’, ‘unregistered’ or ‘informal’. Although producers’ groups and collectives have been considered a way forward in promoting gender empowerment in the informal sector, the process to organize and develop these grass-root initiatives are challenging in a variety of ways, some of the impediments arising from women’s lack of bargaining power with outsiders and lack of internal inclusiveness of its own members. The purpose of this article is to advance discussion on women’s narratives of empowerment by exploring the case of Gram Mooligai Company Limited (GMCL). GMCL is the first female community enterprise in India active in the herbal sector, entirely formed and managed by untouchables. The findings show that GMCL enhances women’s productive capabilities, leadership skills and to some extent social learning abilities, but falls short to confronting marginalization resulting from issues of caste embedded in established patriarchal norms and practices. This case study points to the significance to adopt a more holistic approach, which conceives empowerment as a dynamic, socio-culturally constructed process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".