Pervasive Indigenous and local knowledge of tropical wild species
Bibliographic record
Abstract
The promise of Indigenous and local knowledge (ILK) for conservation policy depends on how pervasively ILK is held among local people. In the Peruvian Amazon, we conducted a landscape-scale concordance analysis between (1) ILK for game, timber, and fish species collected by the largest representative ILK survey as yet undertaken in tropical forests, and (2) remotely sensed land cover as proxies for species habitat. From our survey among 4000 households in 235 communities, we find that concordant ILK is highly pervasive across gender, age, place of origin, and social status, irrespective of species and people's indigeneity. Resource users possess more concordant knowledge than nonusers for timber and fish, not game. Concordance between ILK for fish and remote sensing is associated with cooperative forest clearing in shifting cultivation-an informal community institution in which forest peoples engage with nature. Our findings point to the promise of ILK for large-scale tropical conservation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".