Inclusive Ecosystems? Women’s Participation in the Aquatic Ecosystem of Lake Malawi
Bibliographic record
Abstract
Ecosystem services and their role in alleviating poverty are centered on a set of gendered social relations. The understanding of these relations between men and women in aquatic ecosystems can unveil gender-based opportunities and constraints along the value chains of the ecosystem services. A gender discourse perspective on participation of actors of an ecosystem can further facilitate the understanding of the complex and subtle ways in which gender is represented, constructed, and contested. This paper analyses the barriers to the participation of women in the fishing industry. The analysis is based on a study conducted in five fishing villages of Lake Malawi through a structured questionnaire, focus group discussions, key informant interviews, and observations. First, it looks at gender and participation from a theoretical perspective to explain how gender manifests itself in participation and interrogates why women have limited benefits from the fishing industry. Second, it highlights the barriers that seem to preclude women from participating, which include institutional embedded norms, financial, socio-cultural, and reproduction roles. In general, women had little influence on the type of fishing sites, markets, and access to financing of their businesses. A gender transformative agenda is therefore required to proactively facilitate changes of some entrenched institutional norms as well as having greater access to financial services and new technologies in order to enhance women’s full participation and equal benefits from ecosystem services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".