Rural Women in Livestock and Fisheries Production Activities: an Empirical Study on Some Selected Coastal Villages in Bangladesh
Bibliographic record
Abstract
Progressive participation of women in agriculture is evident throughout the Globe. Their participation in fisheries and livestock sector is well recognized but less perceived due to paucity of sufficient data. Considering this fact the present study had been designed to examine women‟s participation in fisheries and livestock activities as well as influence of selected socio-economic factors on their participation in some selected coastal villages of Bangladesh. For achieving research objectives a well structured interview schedule was administered on 70 randomly selected rural women during the period of September, 2010. Descriptive statistics exhibit that almost three quarter of the respondents had moderate to high level participation in fisheries and livestock activities. Among the fisheries related activities women had highest participation in feed application (M=1.528) while cleaning cattle shed (M=2.914) and giving feed to poultry birds (M=4.571) occupied the top ranks for cattle and poultry related activities. According to correlation estimates agricultural knowledge and family size had strong positive correlation with women‟s participation where as education and family income had negative significant correlation with women‟s participation in fisheries and livestock activities. Stepwise multiple regression mirrored that family size, agricultural knowledge and education jointly contribute to 25.6% variance in women‟s participation in fisheries and livestock activities.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".