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Record W3158511735

Disability and Contractual Expectations

2021· article· en· W3158511735 on OpenAlexaff
Jonas-Sébastien Beaudry

Bibliographic record

VenueSSRN Electronic Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsFiduciaryMoralityRealmEconomic JusticeSocial contractLaw and economicsSubject (documents)SociologyIntellectual disabilityPoliticsPositive economicsPolitical scienceLawEconomicsPsychologyDuty
DOInot available

Abstract

fetched live from OpenAlex

This is a precis of the forthcoming book, The Disabled Contract: Severe Intellectual Disability, Justice and Morality. It examines how people with severe intellectual disabilities (PSID) fare within the social contract tradition. More specifically, it contends that even recent strategies that attempted to integrate disability within the realm of contractual justice and morality are not entirely successful. These strategies cannot convincingly ground a robust moral status for PSID; or, if they do so, it is at the cost of making this status merely derivative or contingent. The failure of social contract theory to bring severe disabilities within its purview should not be seen as a marginal theoretical defect affecting only a small segment of human populations. At best, it reveals a gap that should impel moral and political theorists to give fiduciary and caring ideals their due weight next to contractual ideals. At worst, the social contract tradition is not only incomplete, but necessarily creates and oppresses the ‘disabled subject’. The goal of this precis is to introduce readers to some of the conclusions I reach in the book in an accessible, short format. The arguments are therefore illustrative rather than exhaustive.

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)0.010
metaresearch head score (Gemma)0.017
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 score0.013
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.026
Scholarly communication0.0110.010
Open science0.0010.006
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0130.002

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 head0.013
GPT teacher head0.310
Teacher spread0.297 · 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
Published2021
Admission routes1
Has abstractyes

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