A History Of Women Migration & The Livelihood Challenges Of Migrant Women Working In The Fish Processing Industries
Bibliographic record
Abstract
Migrants are the biggest portion of the huge unorganized labor market in India. There are many inherent obstacles to theirentrance into the labor sector. Deprived of essential skills, knowledge and negotiating power, migrant workers arefrequently caught up in exploitative working arrangements that compel them to work in low-end, low-value, dangerousjobs. This issue is exacerbated by the lack of identification and legal protection. The difficulties of migratory workers aremore widespread when state borders are crossed and the distance between the "source" and "destination" grows. Migrantsmay also be easy victims of politics of identity and parochialism. Economic development in India now depends on labormobility. Migrant workers' contribution to national revenue is huge yet nothing is being done in exchange for their safetyand well-being. Fish/prawn processing industries were set up all along the coastal areas because of the growing; theimportance of this Industry as a source of foreign exchange. This industry all over India prefers migrant contract labourers.The term fishery also comprises one more stock of fish that can be traded as a unit for purposes of conservation andmanagement. The fishery is a stock or stock of fish and the enterprises that have the potential of exploiting them. Becauseof pearl fishing in the city, Thoothukudi is known as "Pearl City." It is a commercial seaport serving South India's interiortowns and is one of Tamil Nadu’s Sea passages. The Present study brings out The Study of Migrant Women / Girls Workingin the Fish/Prawn Industries in Thoothukudi District, Tamilnadu
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".