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Record W4395473110 · doi:10.1515/9782760553620

Question sociale et citoyenneté

2020· book· fr· W4395473110 on OpenAlexfundaboutno aff
Martin Petitclerc, Louise Bienvenue, David Niget, Martin Robert, Cory Verbauwhede

Bibliographic record

VenuePresses de l'Université du Québec eBooks · 2020
Typebook
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaConnaught FundInternational Labour OrganizationUniversity of TorontoUniversity of Oxford
KeywordsPolitical scienceSociology

Abstract

fetched live from OpenAlex

Les régimes de citoyenneté sont traversés par une tension constitutive entre, d’une part, les promesses de liberté et d’égalité et, d’autre part, l’expérience des multiples formes de dépendances et d’inégalités sociales. Cette tension, à l’origine de la dynamique particulière des relations de pouvoir dans les démocraties libérales, engendre la production incessante de régulations sociales afin d’assurer la relative coordination de l’agir individuel et collectif. Les auteurs et autrices de Question sociale et citoyenneté se sont inspirés de cette problématique afin de proposer des analyses historiques sur la régulation d’une variété de problèmes sociaux au Québec et en France. Ils et elles invitent plus largement à découvrir un territoire fertile pour la recherche, soit celui d’une histoire politique des conflits ayant pour objet le gouvernement du monde social au sein des régimes de citoyenneté. Offrant de nouvelles perspectives de recherche, cet ouvrage intéressera tout autant la communauté universitaire que le grand public désirant mieux comprendre l’histoire des politiques sociales et des institutions de prise en charge des inégalités, des marginalités et des déviances.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.037
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0090.065
Scholarly communication0.0160.011
Open science0.0010.007
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0110.001

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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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 designNot applicable
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

Citations0
Published2020
Admission routes2
Has abstractyes

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