Improving fisheries estimates by including women’s catch in the Central Philippines
Bibliographic record
Abstract
Small-scale fisheries catch and effort estimates are often built on incomplete data because they overlook the fishing of minority or marginalized groups. Women do participate in small-scale fisheries and often in ways distinct from men’s fishing. Hence, the inclusion of women’s fishing is necessary to understanding the diversity and totality of human fishing efforts. This case study examines how the inclusion of women’s fishing alters the enumeration of fishers and estimations of catch mass, fishing effort, and targeted organisms in 12 communities in the Central Philippines. Women were 42% of all fishers and contributed approximately one-quarter of the fishing effort and catch mass. Narrower definitions of fishing that excluded gleaning (gathering of benthic macroinvertebrates in intertidal areas) and part-time fishing masked the participation and contribution of most women fishers. In this case study, it is clear that overlooking women and part-time or gleaning fishers led to the underestimation of fishing effort and catch mass. Overlooking gleaning had also led to underestimation of shells and other benthic macroinvertebrates in fishing catches.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".