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Record W2279874911

Beyond Domination: On Republicanism's Exclusionary Entanglements

2010· article· en· W2279874911 on OpenAlexaff
Kiran Banerjee

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOppressionInjusticePluralSociologyPoliticsIdeal (ethics)LawLaw and economicsEpistemologyPolitical scienceEnvironmental ethicsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This paper assesses Phillip Pettit’s claim that neo-republicanism offers a coherent approach for our contemporary political landscape. I challenge the view that non-domination can serve as singular ideal of government and that republicanism offers a superior alternative to the liberal framework. I argue that Pettit’s homogenizing and monistic account of oppression as domination is problematic, as it fails to capture legitimate forms of injustice and relevant claims for redress. To demonstrate this, I develop J.S. Mill’s account of social oppression as a paradigmatic case of the injustices that republicanism is incapable of adequately addressing. Such forms of oppression cannot be identified by the narrow conception of domination that republicanism provides, nor can these exclusions be effectively contested when the language of non-domination reigns. Only by moving to a more plural set of ideals can we accommodate the diversity of voices in modern society and attend to the multiple forms of injustice that must be addressed. But doing so erodes the clear distinction between neo-republican theory and the family of values that we associate with the liberal tradition, in effect re-casting republicanism as a far less radical break.

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)0.007
metaresearch head score (Gemma)0.008
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 score0.017
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0170.066
Scholarly communication0.0080.013
Open science0.0010.021
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.321
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

Citations0
Published2010
Admission routes1
Has abstractyes

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