Bibliographic record
Abstract
Traditional ecological knowledge (TEK) is defined as a deep understanding of the environment developed by local communities and indigenous peoples over generations. In the United States, Canada, and around the world, indigenous peoples are increasingly advocating for incorporation of TEK into a range of environmental decisionmaking contexts, including natural resource and wildlife management, pollution standards, environmental and social planning, environmental impact assessment, and adaptation to climate change. On October 31, 2018, ELI hosted an expert panel on TEK, co-sponsored by the National Native American Bar Association and the American Bar Association Section of Environment, Energy, and Resources. The panel discussed the challenges that indigenous peoples face in defending the legitimacy of, and intellectual property in, TEK; how policymakers can modify existing laws and regulations to better incorporate TEK; and the potential for TEK to meet today's most pressing environmental challenges. Below, we present a transcript of the discussion, which has been edited for style, clarity, and space considerations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.097 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".