Bibliographic record
Abstract
Trained as a cultural anthropologist, his research interests rest largely in environmental and economic anthropology.David's training and professional experience lie predominantly in qualitative and community-based research.His experience has been cultivated through applied research partnerships with Aboriginal communities in Alaska and Canada.This is where he has had the opportunity to research and publish on the various challenges faced by rural communities, the changing northern economy, and the strategies employed by Aboriginal and other resource dependent communities to deal effectively with social, political, economic and environmental change.By working directly with Aboriginal resource users, tribal governments, federal, state and provincial government agencies, and resource development industries, David has gained considerable insights.This has included issues relating to indigenous systems of land tenure (particularly in relation to traditional ecological knowledge), the politics of resource allocation, and how power is articulated, and best negotiated, in contested environments.He has an extensive CV that includes a significant list of publications, including books, book chapters, and journal articles.Much of the information in this
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 | 0.016 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.026 | 0.023 |
| Insufficient payload (model declined to judge) | 0.064 | 0.036 |
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".