IMMEDIATE EFFECTS OF COVID-19 PANDEMIC SITUATION ON LIVELIHOOD OF WOMEN CULTIVATORS IN THE COASTAL AREAS OF WEST BENGAL AND ODISHA
Bibliographic record
Abstract
India is known for its dependence on agriculture and allied sectors as the major pillar of its economy. Being one of the major emerging nation in the world’s economic and political power scenario the country has been expected to stand in good position in terms reduction of poverty and livelihood development of the people particularly of the rural area. The spread of Covid-19 virus has attributed to huge economic squander worldwide and India is no more any exception, rather the economy is such badly affected due to this situation that the GDP growth has brought down to only 23.9% over the same quarter last fiscal. Due to the current pandemic situation a large portion of global population is in a deadly juncture for its livelihood options due to sudden unemployment and related poverty along with. High risk of getting infected by this deadly virus and it’s after effects. Specifically, the poor rural community who are largely dependent on agriculture and labour work are the worst hit ones due to this present situation. In this perplexing time being the vulnerable section women are again the most affected ones. This paper aims to understand the current situation and its immediate effect on the women cultivators of coastal parts of West Bengal and Odisha; the study also aims to find major facts towards their attitude of alternation of livelihood strategies. The study has been done exclusively on primary data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".