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Record W4237455223 · doi:10.1080/0305724042000200047

The legacies of liberalism and oppressive relations: facing a dilemma for the subject of moral education

2004· article· en· W4237455223 on OpenAlexaff
Dwight Boyd

Bibliographic record

VenueJournal of Moral Education · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSubjectivitySociologyOppressionEpistemologySubject (documents)Moral disengagementPoliticsLiberalismSocial cognitive theory of moralityEnvironmental ethicsLawPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

In modern Western moral and political theory the notion of the liberal subject has flourished as the locus of moral experience, interpretation and critique. Through this conceptual lens on subjectivity, individuals are enabled to shape and regulate their interactions in arguably desirable ways, e.g. through principles of respect for persons and the constraints of reciprocal rights, and moral education has largely adopted this perspective. However, this article argues that some kinds of morally significant relations—those framed by social groups related to each other through structures of hierarchical power—constitute a different kind of subjectivity that needs more theoretical and empirical attention. In contrast to four core characteristics of liberal subjectivity, a view of subjectivity that can be located in how individuals are members of particular kinds of social groups is offered. It is argued that unless it can accommodate working with attention to this form of subjectivity as well, moral education runs the risk of itself contributing to forms of oppression such as racism, instead of being a means of combating them.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0130.199
Scholarly communication0.0240.029
Open science0.0020.017
Research integrity0.0120.021
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.069
GPT teacher head0.373
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), 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

Citations73
Published2004
Admission routes1
Has abstractyes

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