Traditional Ecological Knowledge and Global Environmental Change: Research findings and policy implications
Bibliographic record
Abstract
entitled "Traditional Ecological Knowledge and Global Environmental Change. The special feature addresses two main research themes. The first theme concerns the resilience of Traditional Ecological Knowledge (hereafter TEK) and the conditions that might explain its loss or persistence in the face of global change. The second theme relates to new findings regarding the way in which TEK strengthens community resilience to respond to the multiple stressors of global environmental change. Those themes are analyzed using case studies from Africa, Asia, America and Europe. Theoretical insights and empirical findings from the studies suggest that despite the generalized worldwide trend of TEK erosion, substantial pockets of TEK persist in both developing and developed countries. A common trend on the studies presented here is hybridization, where traditional knowledge, practices, and beliefs are merged with novel forms of knowledge and technologies to create new knowledge systems. The findings also reinforce previous hypotheses pointing at the importance of TEK systems as reservoirs of experiential knowledge that can provide important insights for the design of adaptation and mitigation strategies to cope with global environmental change. Based on the results from papers in this feature, we discuss policy directions that might help to promote maintenance and restoration of living TEK systems as sources of social-ecological resilience.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".