Robert Bernasconi and the challenges of a Critical Philosophy of Race: (Un)learning to read and teach the history of moral philosophy
Bibliographic record
Abstract
This essay is an attempt to determine what Robert Bernasconi's body of work in Critical Philosophy of Race can teach us about the way in which we, philosophers and professors of philosophy, ought to treat our institutional heritage. What should we make, for instance, of moral claims made by philosophers of the modern era who - tacitly or explicitly - manifested certain levels of endorsement toward the Atlantic Slave Trade? How should we comprehend the conceptual tools that we have inherited from them, knowing that those were formulated alongside justificatory claims for the enslavement of Africans - claims that we now deem undoubtably and universally immoral? I extract from Bernasconi's writings an implicit methodology that can be broken down into three main moves: (1) a historiographical work, akin to Michel Foucault's 'archaeological' method, aimed at uncovering the material conditions that allowed for the emergence of philosophical ideas of the past, (2) a dialectical work aimed at interpreting this collection of historical data through the critical lens of race, and (3) a pedagogical work aimed at transforming the practice of academic philosophy in light of the critique. I conclude that his methodological contribution culminates in an invitation to revisit and transform the past of the institution by treating the history of academic philosophy as philosophically and conceptually relevant rather than merely incidental. Such a commitment toward critically engaging the past of our institution urges us to revisit the canon in specific ways.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".