A systematic review of resource habitat taboos and human health outcomes in the context of global environmental change
Bibliographic record
Abstract
The dependence of humans on the ecosystem services that natural resources provide is absolute. The need for social taboos as frameworks for governing natural resource abstraction is gaining widespread recognition especially within the context of climate change. However, the complex relationship between resource and habitat taboos (RHTs) and human health is not entirely understood. We conducted a systematic review of existing studies of the association between RHTs and human health outcomes, focusing on the best evidence available. We searched JSTOR, SocINDEX, Greenfile and Academic Search Complete databases from 1970 to July 2015; and also searched the reference lists of reviews and relevant articles. About 779 studies and data from 26 studies were eligible for the analysis. Only 9 out of 26 studies clearly linked RHTs to human health. Overall, nine taboos, spatial, temporal, gear, method, effort, catch, species-specific, life history and segment, were covered by the empirical studies. This systematic review provides new evidence of relationships between RHTs and human health outcomes. Several methodological limitations were identified in the empirical material. The findings suggest the need for context-specific conservation policies to reduce erosion of RHTs in order to sustain human health in the face of climate change.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".