Gender equity and collaborative care in Madagascar’s locally managed marine areas: reflections on the launch of a fisherwomen’s network
Bibliographic record
Abstract
Collaborative care refers to the collectively formed reciprocal relationships that emerge between the human and more-than-human world. Such care relations are more robust when individuals from different socioeconomic, gender, and political stratifications participate in decision making. This condition applies to gender equity in ocean conservation and fisheries governance practices. Despite their deep involvement in marine fisheries and their labor in sustainable fisheries management and marine conservation, women are often underrepresented in and overlooked by environmental management institutions. Highlighting the importance of gender to effective marine management underscores the need to reconfigure leadership in marine resource governance, and to reconsider how that leadership is understood. To investigate how gender affects community-based conservation, this paper explores marine management practices in Madagascar. Incorporating conversational method and auto-ethnography, we center the expertise and experience of Malagasy women leaders, who have been deeply involved with the foundation and management of a gender-inclusive community marine resource management network. This is especially relevant to Madagascar, where locally managed marine areas (LMMAs) have expanded dramatically in the last decade. Many of these LMMAs have explicitly focused on reconfiguring power relations between international conservation efforts and local resource user needs and values. Overall, the LMMA approach has improved local involvement in resource management decisions, yet areas of weakness remain. We argue that a more inclusive, and thus more effective, approach to governing marine commons requires a focus on the act of commoning: the process through which reciprocity, accountability, and collaborative care are developed within a community. To achieve whole-community governance, we advocate for allocating more resources toward such commoning practices and toward those who are most marginalized in current marine management. Madagascar’s evolving network of fisherwomen leaders provides key insights into how interventions for commoning in marine conservation can advance collaborative care of interdependent human-environment systems.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.033 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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".