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Inclusive Citizenship Beyond the Capacity Contract

2017· reference-entry· en· W2785253942 on OpenAlexaff
Will Kymlicka, Sue Donaldson

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsQueen's University
Fundersnot available
KeywordsCitizenshipDeliberationSocial contractInterdependenceMainstreamAgency (philosophy)PoliticsLaw and economicsSocial citizenshipSociologyPolitical scienceLawEpistemologySocial science

Abstract

fetched live from OpenAlex

There is deep tension within mainstream citizenship theory. On the one hand, citizenship is often defined in terms of social membership, such that all those affected or all those governed should be part of the demos. On the other hand, citizenship is often limited by an implicit “capacity contract” to those with sophisticated cognitive and linguistic capacities to engage in rational political deliberation, thereby excluding children, people with cognitive disabilities, and animals, who are relegated to a nebulous (and neglected) status of wardship. This chapter explores this tension between these two accounts, and argues that we should abandon the capacity contract as both theoretically arbitrary and politically pernicious. Citizenship should include all members of society, and this in turn requires new models of (interdependent) agency that enable all members to participate in shaping the society and laws by which they are governed.

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)0.010
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: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.044
Scholarly communication0.0120.016
Open science0.0010.012
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0080.001

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 head0.114
GPT teacher head0.372
Teacher spread0.257 · 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
GenreOther

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

Citations30
Published2017
Admission routes1
Has abstractyes

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