Constructing confidence: rational skepticism and systematic enquiry in local ecological knowledge research
Bibliographic record
Abstract
Key attributes of the social research contributions on indigenous ecological knowledge (IEK), local ecological knowledge (LEK), and traditional ecological knowledge (TEK) are analyzed using the most frequently cited literature generated by the "ISI Web of Knowledge" and "Google Scholar" search engines. They are further exemplified by an examination of two contrasting approaches to the analysis of IEK/LEK/TEK. The results show that IEK/LEK/TEK is treated predominantly via definitions, and few articles examine concepts, research design, methods, or operational attributes. Consequently, there is no consensus on the content of IEK/LEK/TEK, the primary components of which await examination via focused research. These are fundamental issues, since IEK/LEK/TEK misrepresented by social research would probably deepen disempowerment of those it purports to champion. Research topics are suggested to address these issues.
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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 | 0.300 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.012 | 0.159 |
| Scholarly communication | 0.026 | 0.037 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".