Bibliographic record
Abstract
This contribution addresses key issues around the application of Indigenous knowledge in contexts where such knowledge is neither generated nor held (academy, industry, governments, etc.). Effective models for the ethical incorporation of Indigenous knowledge into environmental governance in Canada have remained elusive despite decades of attempts. The predominant research paradigm of “incorporating” Indigenous knowledge into environmental governance is one of extraction by the external interests who seek to include specific aspects of such knowledge in their undertakings. This approach continues to fail because Indigenous knowledge exists as an integral component of Indigenous Knowledge Systems (IKS). It is often hollow and potentially damaging to consider any knowledge without understanding the societal systems and peoples that produced it. Indigenous knowledge is not just “knowledge” (a noun) but a way of life, something that must be lived (a verb) in order to be understood. Indigenous knowledge is inseparable from the people who hold and live this knowledge. Although government policy and legislation have evolved in attempts to treat Indigenous knowledge more holistically, the overriding paradigm of extraction remains essentially unchanged. Even the most recent frameworks will meet with limited success as a result. Appropriate and effective inclusion of Indigenous knowledge requires recognition of the systems that support it, which in turn necessitates support for Indigenous self-determination.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".