Livelihood Of Fisherwomen Community - An Analysis
Bibliographic record
Abstract
The worldwide pandemic situation, COVID – 19 has unpredictable impacts on all the sectors of the economy all over the world and is no exception to the fishing sector. The fishing sector plays a very crucial role in the economy by satisfying food and employment to millions of people and narrating the cultural identity of many coastal communities and contributing to keeping them fishing communities across India. A sudden India-wide lock-down, with just four-hour notice during COVID – 19 outbreak turned many Indian fisherwomen's livelihood upside down. Though the lockdown may help reduce the spread of coronavirus; but has a chronic impact on the livelihood of vulnerable populations i.e. fisherwomen, particularly on food systems, storage, and market chains both locally and regionally. Mumbai has a wide coastal line on which most of people depend on fishing for their bread and butter. As an impact of the current pandemic, a number of fisherwomen lost their employment and suffered increasing poverty and inequality. With a large number of fishing days lost because of Cyclone in 2019 and now the pandemic, fisherwomen have been looking for support to run their daily lives. They still await their full- fledge fishing days to begin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".