The Role of Socioeconomic Factors, Psychological Motivations, and Social Networks in Women’s Participation in Community-Based Fishery Management in Ghana
Bibliographic record
Abstract
Women’s participation in community-based decision-making in managing natural resources such as fishery is considered crucial to the sustainability of the fishery industry. However, women experience the brunt of inequality in fishery tasks and decision-making despite their crucial contributions. Using survey data (N = 400) from an ethnographic study on Ghanaian female fisherfolk, we examine the factors affecting women’s participation in community-based fishery decision-making. Findings show that while women attended community meetings, only a few held positions in the fishery associations. Whilst education was not a significant factor in women’s community participation, the age of children, women’s ownership of fishery assets as well as psychological characteristics such as trust, interests and gender role attitudes were very crucial. Results further show that network variables such as women’s position in other associations were more important to their participation in community-based fishery decision-making than mere membership in such associations. This study highlights the need for fishery policies aimed at gender equality to move beyond gender structures and economic models toward examining the complexity of factors affecting different aspects of women’s participation in fishery decision-making.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".