A Comparative Social Study among Artisanal Subsistence Marine Fishers of Sundarbans, Paradeep and Chennai
Bibliographic record
Abstract
This paper does a comparative study of the socio-economic condition of the artisanal subsistence marine fishers of the three locations of India namely Sundarbans, Chennai and Paradeep. The small-scale artisanal marine fishers of Chandrapur, Gobindapur, Kalinagar, Ganeshpur villages of Kakdwip Subdivision, Sundarbans along with that of Badakalikhada, Bijaychandrapur, Light House and Kharinali, of Paradeep and Nochi Nagar, Light House, Foreshore Estate, Mandaveli, Mylapore of Chennai were asked the standardised questions. Sundarbans, Paradeep and Chennai have three distinct socio-economic conditions prevailing due to the privatisation of the fishery industry and the distinct difference in income.The purpose of this study is to find the socio-economic condition of the artisanal marine fishers of the Sundarbans, Paradeep and Chennai. A descriptive rapid sampling method has been followed, where the fishers have been questioned about their respective socio-economic status. The paper studies the income difference and the effect of the usages of motorised boats in two three-year slots 2015-2018 and 2018-2020 affluence due to more exposure to the technical changes. The study found that the Chennai fishers were more adept to take the advantage of the new technologies of marine fishing thus far more economical stable, the Paradeep fishers were second in economic stability due to their comparative adaptability. The Sundarbans fishers were least adept with the technological changes in marine fishing, consequently they are least affluent.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".