Mobile apps increase the visibility of women’s work contributions in Mexican small-scale fisheries
Bibliographic record
Abstract
Small-scale fisheries in low and middle-income countries often lack information on data reporting processes and reliable data sources. Accurate data collection is crucial for accountability, as it helps track reporting sources and monitor fishing activities. Current empirical studies on the effectiveness of mobile apps in facilitating transparent data sharing, addressing gender disparities, and improving compliance with marine reserves remain limited. To address these challenges, the non-governmental organization Comunidad y Biodiversidad launched PescaData in 2020, a mobile app designed to help fishers record logbooks, share community solutions, and access a marketplace for trading, knowledge exchange, and communication. This study examines the potential of PescaData to enhance the visibility of women's contributions to small-scale fisheries, using marine reserves in Mexico as a case study. The research employs Q-methodology, a participatory mixed-methods approach that identifies shared perspectives within a group. Using generic purposive sampling, 10 fishery leaders participated, representing general opinions on PescaData’s impact. Findings highlight two key perspectives. First, mobile apps like PescaData increase the visibility of women in fisheries, fostering trust and collaboration between male and female fishers and fish workers, strengthening collective action. Second, as trust grows, male fishers begin to delegate traditionally male-dominated tasks, such as reporting catch data, to women. This shift enables women to take on more active roles in reporting both community solutions and catch data, which leads to increased compliance with marine reserves in the fishing community. These findings emphasize the transformative potential of mobile technology to promote gender inclusivity and sustainability in small-scale fisheries. By integrating digital tools like PescaData, fisheries management can enhance data transparency, accountability, and conservation efforts while fostering equitable participation among fishery actors. The study underscores the need for further research and policy to maximize the benefits of mobile technology to achieve sustainable and inclusive fisheries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| 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".