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Record W4409723210 · doi:10.4000/13sva

Capitalisme et publicité contestée. Une conversation avec Nancy Fraserespace public, contre-public, hégémonie, capitalisme, classe, race, intersectionnalité, réseaux sociaux, plateforme

2024· article· fr· W4409723210 on OpenAlexvenueno aff
Victor Kempf

Bibliographic record

VenueCommuniquer Revue de communication sociale et publique · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsCONTESTConversationArtSociologyPolitical scienceCommunicationLaw

Abstract

fetched live from OpenAlex

À la suite d’un atelier consacré à la réinvention de l’espace public (« Wildening the Public Sphere ») avec Nancy Fraser, organisé au Berlin Centre for Social Critique en juin 2022, nous avons eu l’opportunité de prolonger la discussion lors d’un échange par visioconférence en novembre de la même année. Notre discussion a commencé par l’analyse des liens entre les « contre-publics subalternes » et le « public en général », portant notamment sur l’émergence des contre-publics de droite et l’influence supposée des prétendus « réseaux sociaux » sur l’espace public. Cette réflexion nous a ensuite conduits à interroger la manière dont les différents publics s’inscrivent dans le système capitaliste, tout comme leur capacité à politiser des enjeux qui, dans les sociétés capitalistes, sont traditionnellement relégués à la sphère privée. Une telle problématique revêt une importance particulière dans un contexte où la construction d’une identité de classe ouvrière élargie, non essentialiste, et capable de fédérer une diversité de formes de travail2, constitue un enjeu politique majeur. C’est ce qui ressort de l’analyse de Fraser dans sa récente « conférence Benjamin » (Benjamin Lecture)3 (2022a).

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)0.003
metaresearch head score (Gemma)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.027
Scholarly communication0.0160.014
Open science0.0010.005
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 head0.157
GPT teacher head0.427
Teacher spread0.270 · 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
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
Published2024
Admission routes1
Has abstractyes

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