Bibliographic record
Abstract
Abstract Motivated ignorance is an incentivized absence of knowledge that arises in circumstances of unequal power relations, a self-protective non-knowing which frees individuals from having to reflect on the privileges they have in virtue of membership in a dominant social group. In philosophical discussions, the term “motivated ignorance” gets used interchangeably with “willful ignorance.” In the first half of this paper, using Charles Mills’ (2007) white ignorance as the defining case, I argue that this is a mistake. A significant swath of cases of motivated ignorance are non-willful, or deep, following Rik Peels (2010). But in all cases, benefits accrued to some in virtue of their social position are gained and maintained at the expense of harms to others. In the second half of this paper, I argue that these harms are what ground attributions of culpability in cases of motivated ignorance and drive the normative requirement that the subject know better, so long as the facts in question are ordinarily and easily knowable (in a sense to be specified). Willfulness is not a necessary condition for culpability, even if it is a sufficient one.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.046 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".