Ghanaian Couples’ Perceptions and Attitudes Towards Women’s Participation in Family Fishery Decisions and Practices
Bibliographic record
Abstract
In the last few decades, there has been growing scholarly interest about the implications of women’s increased economic role on household decision-making dynamics. Contributing to these conversations, the current paper examines couples (wives and husbands) perceptions and attitudes towards women’s decision-making power in the context of Ghana’s small-scale fishery where both couples engage in the same economic activity, performing different, but complementary roles. The study involved interviews with 20 and 18 married female and male fisherfolk respectively, which was part of a larger study by the first author among fisherfolk in the south-western coast of Ghana. We show that accounts of perceived decision-making power of women varied by spouse. Using a gender lens, we show how both women and men discursively reproduced and challenged hegemonic gender discourses and relations in different contexts. We argue that exploring the gaps in couples’ account of women’s household decision-making power as well as their perceptions and attitudes towards such roles provides important ground to understanding the inconsistencies in the outcomes of programs targeted at women empowerment and gender equality.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".