Gender differences in the perceived impacts of coastal management and conservation
Bibliographic record
Abstract
Abstract Gender influences the ways that people are involved in and rely on coastal resources and spaces. However, a limited understanding of gender differences in this context hinders the equity and effectiveness of coastal management and conservation. Drawing on data collected through purposive sampling from 3063 people in Fiji, Papua New Guinea, Solomon Islands, Indonesia, Kenya, and Madagascar, we explored how men and women perceived the effects of coastal management and conservation on human well-being. We found significant gender differences in perceptions of the presence of impacts, whereby 37% of women and 46% of men perceived individual-level impacts, while 47% of women and 54% of men perceived community-level impacts. When asked about the degree and direction of impacts, the responses were not significantly different by gender. When describing the types of impacts, women and men articulated these differently, particularly impacts related to economic, governance, and health aspects of well-being. These findings highlight pathways for developing more equitable and gender-responsive coastal management and conservation initiatives aimed at safeguarding biodiversity, sustaining fisheries, and supporting the well-being of all those who depend on the marine environment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 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".