Bibliographic record
Abstract
Local knowledge theory and methods: an urban model from Indonesia, Christoph Antweiler Doing and knowing: questions about studies of local knowledge, Andrew P. Vayda, Bradley B. Walters and Indah Setyawati A decision model for the incorporation of indigenous knowledge into development projects, Paul Sillitoe and Julian Barr Triangulation with tecnicos: a method for rapid assessment of local knowledge, Jeffery W. Bentley, Eric Boa, Percy Vilca and John Stonehouse Local history as 'indigenous knowledge': aeroplanes, conservation and development in Haia and Maimafu, Papua New Guinea, David Ellis and Paige West The INGO, the project and the investigation of 'indigenous knowledge': the case of non-timber forest product (NTFP), Sebastian Taylor Indigenous views on the terms of participation in the development of biodiversity conservation in Nepal, Ben Campbell Negotiating change, maintaining continuity: science education and indigenous knowledge in Eastern Canada, Trudy Sable The re-emergence of traditional medicine and health care in post-colonial India and national identity, Subhadra Mitra Channa In dialogue with indigenous knowledge: sharing research to promote empowerment of rural communities in India, R. Baumgartner, G.K. Karanth, G.S. Aurora and V. Ramaswamy Index.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".