Bibliographic record
Abstract
Abstract Pretendianism is a problem in academia (and of whiteness). Its long-standing existence is well researched and analyzed in the academic record, and it has been brought to wider audiences through news and social media. In response, task forces, committees and advisory councils are being created in universities to determine stronger identity validation policies, with emphasis on engaging relationships with local Indigenous nations, communities, elders, and knowledge holders. Policy making, including processes and procedures of identity validation, will be a powerful apparatus going forward to administer indigeneity in universities. This approach will also lead to the intensification of Indigenous definition and regulation by predominantly non-Indigenous institutions. This article proposes a set of complementary extrapolicy practices addressing pretendianism worth exploring and that emerge from the everyday embodied vantage points of Indigenous academics. We must (continue to) name whiteness, model Indigenous relationality and learn from Indigenous women's leadership.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.061 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".