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Record W4206459726 · doi:10.3138/utlj-2021-0086

Discrimination and the value of lived experience in Sophia Moreau’s <i>Faces of Inequality</i>

2022· article· en· W4206459726 on OpenAlexvenueno aff
Erin Beeghly

Bibliographic record

VenueUniversity of Toronto Law Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsExplanatory powerSketchEpistemologyPolitical philosophySociologyPower (physics)PoliticsInequalityValue (mathematics)PhilosophyLawPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.314
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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