Bibliographic record
Abstract
The popular use of the term Indigenous Knowledge to denote an information set has emerged internationally over the last fifteen years. To illustrate how globally and electronically networked Indigenous Knowledge activity has become in this short period, one need only do a Google search on the term. Last week such a search pulled up 2,610,000 hits in 0.07 seconds. A short browse of front pages of the first thirty in the list included references to: newsletters, conferences, scholarly papers, guides, bibliographies, websites, gateways, programs, databases, resource indexes, and registers of best practice. These first thirty also crossed Africa, Ethiopia, Alaska, Brazil, New Zealand, Canada, the US and China. Topics crossed Indigenous Knowledge systems and science, agriculture, land husbandry, forest management, biological diversity, bio-prospecting, intellectual property, community rights, values and protection. The presence of Indigenous Knowledge on the Internet demonstrates considerable engagement and intersections with Indigenous Knowledge issues globally by Indigenous and non-Indigenous interests. There is also a growing scholarship around the issues (Agrawal, 1995a, 1995b; Ellen & Harris, 1996; Eyzaguirre, 2001; Nakata, 2002) and UN activities and mechanisms that seek to support the rights of Indigenous peoples in relation to knowledge (WIPO).
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.018 |
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".