The influence of household socio-economic profiles on the use of mangrove resources along the Benin coastline
Bibliographic record
Abstract
Introduction The mangrove ecosystems are crucial marine ecosystems, providing various ecological and economic benefits to local communities. This study aims to analyze the influence of household socio-economic profiles on mangrove ecosystems resources in Benin. Methods Based on semi-structured interviews, 464 households were surveyed in 73 localities. The Specific Multiple Correspondence Analysis (spMCA) combined with Hierarchical Ascendant Classification (HAC) was used to analyze the socio-economics, demographics, and environmental data collected. The priority scores of the surveyed households were used to perform the influence of households to mangrove resources by coupling HAC to Principal Component Analysis (PCA). Results Five household categories (labeled CM1–CM5) were identified. They represent varying levels of vulnerability, access to resources, and modes of mangrove resource use. The household of CM1 are highly dependent on mangrove resources and distributed proportionally across the study sub-sectors whereas the CM5 household are the most vulnerable, located around the biosphere reserve, and facing land tenure insecurity, extreme poverty, and significant pressure on mangrove ecosystems. The household categories are strongly influenced by the dependency on biosphere reserve resources and sociolinguistic affiliation. The gender and education level play a secondary role on household categories structuring. Five groups of pressure factors on the mangroves were associated with the different household categories. Salt farming, wood cutting, and backfilling of waterways are predominant for CM1, CM2, CM3, and CM5. Land overexploitation, depletion of fishery resources, and climatic factors are the mainly determinants for CM4 and CM5. Land conflicts and declining agricultural yields are also mentioned by CM5. Conclusion These results highlight the need for differentiated mangrove management strategies, tailored to the specific socio-economic characteristics of the households. Co-management, reforestation, and awareness-raising activities, represent strategic targets for sustainable conservation policies. Also, specific support measures, including the development of sustainable economic alternatives and improved access to basic services are needed. Taking this typology into account would improve the effectiveness and equity of environmental policies related to mangrove 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".