Discrimination and the value of lived experience in Sophia Moreau’s <i>Faces of Inequality</i>
Bibliographic record
Abstract
In Faces of Inequality: A Theory of Wrongful Discrimination, Sophia Moreau embarks on a classic philosophical journey. It is what philosophers nowadays call an explanatory project. The goal of explanatory projects is to deepen our understanding of wrongful actions and what they share in common. In this review essay, I argue that Moreau’s book embodies a valuable explanatory project that ought to be on the radar of lawyers, legal theorists, and philosophers. After sketching the book’s arguments, I explain why they are so refreshing. The remainder of the review essay proceeds in a more critical mode. First, I argue that the book’s explanatory aspirations fall short, and I sketch a framework for a more radically pluralistic theory of wrongful discrimination. This framework has the power to embrace Moreau’s compelling view that discrimination wrongs people by failing to treat them as equals while also recognizing a rich array of other discriminatory wrongs found in lived experience. Second, I argue that Faces of Inequality will disappoint readers looking for a truly inclusive account of wrongful discrimination. I end by emphasizing the book’s contribution to political philosophy and its ambition to provide a truly liberatory theory of what we owe to each other as moral and political equals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".