Bibliographic record
Abstract
If we do not resist, we will not survive. Our resistance will guarantee our children a future. – Winona LaDuke Knowledge has not become politicized; it always has been so. Indigenous knowledge systems explicitly recognize this by their responsiveness to the normative aspects of knowledge, to how human power and agency must be constrained if relations of affiliation with other entities are to be acknowledged and maintained – relations which enable our mutual, and multigenerational, survival. Yet the ideology of western science, wedded as it is to the thesis of value-neutrality, insists that issues of power do not enter into knowledge making or shape the dynamics of knowledge systems. The relations of domination and assimilation which characterize imperialism (whether in its historical or contemporary variants), and which facilitate biocolonialism, are thus neither acknowledged nor acknowledgeable. And so the endangered status of indigenous knowledge systems is recognized, but responsibility for it, complicity in it, is denied: [C]ritical analysis of why Indigenous Knowledge is threatened … rarely moves beyond the rather simplistic assertion that the “Elders are dying” or the assumption that IK systems are more vulnerable … because they are oral…The answers to how and why our knowledge has become threatened lie embedded in the crux of the colonial infrastructure. With the aid of such depoliticization, corporate, academic, legal, and governmental institutions pool their interests and immense resources to extract from these knowledge systems what they find valuable in them.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".