Normalizing Alternatives with <i>Frequently Asked White Questions</i>
Bibliographic record
Abstract
This article is the result of a case study of Frequently Asked White Questions ( FAWQ ) by Ajay Parasram and Alex Khasnabish. We examine FAWQ as a tool for fostering meaningful conversations about race and anti‑racism. In order to facilitate social change through books and book talk, a book must be discoverable and visible. Further, for a book to have this kind of social impact, we argue that three things must be true: that the guiding ethos of the publisher is one of progressive politics; that the author(s)’ stories are authentic and accessible; and that communication about the book’s contents between the publishing agents, authors, and readers follows a politic of love. Situating this discussion within the current political environment, we highlight the urgency of such conversations. Drawing from Paulo Freire’s concept of dialogue as foundational to social transformation, we argue that by normalizing discomfort and centering relational engagement, the book offers a model for navigating difficult discussions in ways that resist ideological rigidity and promote sustained, everyday activism.
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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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".