GENDER AND FISHERIES : THE REPUBLIC OF HAITI
Bibliographic record
Abstract
Fisheries in Haiti are relatively underdeveloped, with women making critical yet often underappreciated contributions, predominantly as fish processors and traders (‘machanns’ and ‘madan saras’). Beyond these roles, women invest in fishing activities, assist with fishing trip preparations, build and maintain fishing equipment, innovate in food processing and storage, engage in shore-based harvesting, generate alternative income streams and increasingly contribute to fishers’ associations. Haiti is unique among Caribbean nations for having ministerial level government structures tasked with improving the status and rights of women, combined with a constitutional requirement that 30% of elected and appointed national positions be held by women. Grassroots organizations and women's rights activists have made substantial progress in advocating for gender equality. Nevertheless, female political participation remains limited and gender-based violence is a serious and persistent concern. Ongoing socio-political instability and Haiti’s high vulnerability to natural disasters has further hindered progress. It is important to note that the deteriorating security situation manifests differently in urban and rural areas, with most challenges concentrated around urban centres and fishing communities located in mostly rural settings. This fact sheet provides an overview of the role of seafood production in Haiti, with a focus on gender dimensions, highlighting opportunities to strengthen gender equity and women’s empowerment in the sector and beyond. It is part of a series meant to offer development agency employees, government agencies, NGOs, funders, and researchers, with a snapshot of gender and fisheries to inform the planning and delivery of relevant activities these actors might be involved in or are in the process of developing.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".