Assessing the Effect of Marine Reserves on Household Food Security in Kenyan Coral Reef Fishing Communities
Bibliographic record
Abstract
Measuring the success or failure of natural resource management is a key challenge to evaluate the impact of conservation for ecological, economic and social outcomes. Marine reserves are a popular tool for managing coastal ecosystems and resources yet surprisingly few studies have quantified the social-economic impacts of marine reserves on food security despite the critical importance of this outcome for fisheries management in developing countries. Here, I conducted semi-structured household surveys with 113 women heads-of-households to investigate the influence of two old, well-enforced, no-take marine reserves on food security in four coastal fishing communities in Kenya, East Africa. Multi-model information-theoretic inference and matching methods found that marine reserves did not influence household food security, as measured by protein consumption, diet diversity and food coping strategies. Instead, food security was strongly influenced by fishing livelihoods and household wealth: fishing families and wealthier households were more food secure than non-fishing and poorer households. These findings highlight the importance of complex social and economic landscapes of livelihoods, urbanization, power and gender dynamics that can drive the outcomes of marine conservation and management.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".