Perceptions of gender dynamics in small‐scale fisheries and conservation areas in the Pursat province of Tonle Sap Lake, Cambodia
Bibliographic record
Abstract
The Tonle Sap Lake of Cambodia is one of the most productive ecosystems in the world, supporting millions of small‐scale fisher livelihoods. Women's contributions in these fisheries are often overlooked due to socio‐cultural expectations of roles and responsibilities. This is a crucial omission since climate and anthropogenic influences increasingly threaten lake inhabitants. Addressing these challenges requires the full participation of both men and women who use the lake, thus it is necessary to first understand the social dynamics of these communities. We investigated whether there were differences between men's and women's perceptions of (i) fishing and non‐fishing practices; (ii) power, access and control over fishing resources; and (iii) perceptions towards conservation and conservation areas in Pursat, Cambodia. We interviewed fishers and key informants, and found differences in perceptions of fishing and non‐fishing practices between fishermen and fisherwomen. Men more openly acknowledged unequal power dynamics, access to and control over fishing resources when compared with women. We found contrasting ideas of community fisheries and conservation between men and women, and health and safety challenges they faced in conservation areas. Findings suggest that community perspectives and unequal power relations established specific roles for women that limited their active participation in fisheries management.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| 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".