Strategic ignorance, is it appropriate for indigenous resistance?
Bibliographic record
Abstract
In The Racial Contract, Charles Mills introduces the notion of an ‘inverted epistemology,’ an epistemology that construes social and racial ignorance as knowledge (p.18). As Mills points out, such ignorance can be used to oppress people by creating alternate realities or ‘white mythologies’ about race (p. 19). If the racial contract results in a society that oppresses people of color and supports white supremacy, then the question of how to correct an inverted epistemology becomes critical. Mills proposes the correction of history and the racist thinking underwriting it as a solution to the problem. Others, like Alison Bailey, seek to confront racial ignorance with active resistance by suggesting that individuals from historically marginalized groups exploit what members of dominant society don’t know (p. 77). While Bailey’s solution, referred to as strategic ignorance, may originate from good intentions, I argue that it is in general inappropriate for Indigenous people. Strategic ignorance is a product of the Western worldview, and it privileges Western values. In fact, both solutions offered by Mills and Bailey fail to consider Indigenous values. Mills’ proposed solution, however, is less objectionable. Instead of either solution, a more suitable form of resistance – one that embraces connectedness, balance, and harmony – would be ideal for the Indigenous communities of Turtle Island.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.066 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".