<i>Thinking While Black: Translating the Politics and Popular Culture of a Rebel Generation</i>, by Daniel McNeil
Bibliographic record
Abstract
Daniel McNeil has provided a detailed, expansive, and inspirational study of Black cultural critics Armond White (an American film and music critic) and Paul Gilroy (a British cultural critic). Going beyond an exploration of their work, McNeil delivers a nuanced investigation into these thinkers, considering how they evolved from ‘young soul rebels’ to ‘middle-aged mavericks’ (p. xvII). McNeil follows them as they rock against racism in the 1970s all the way to their current careers, reacting to the ever-changing world around them. Thinking While Black spans continents and decades of revolution, music, and film as McNeil takes a deep dive into not only the work of these two men, but also of the entire rebel generation to which they belonged. Drawing on hidden and little-known archives of resistance and resilience, this work sheds a new light on the politics and poetics of this generation, and how they came together often outside of conventional politics. Importantly, McNeil explores not only the writings of these men, but also how significant the popular culture they critique and examine is more broadly. This book is not only an exploration of important thinkers, but also it is a testament to the power of Black popular culture. The book also certainly occupies a vacant space in the current academic landscape. Indeed, at the launch, McNeil noted that he was motivated by the question ‘why don’t we know more about Gilroy before he was writing for The Black Atlantic, why don’t we know more about the films that Fanon watched?’ McNeil’s curiosity and desire to fill these gaps are present on every page.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".