Gender differences in the perceived impacts of coastal management and conservation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it