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Record W4214845558 · doi:10.4000/books.pur.146090

Selfies & stars

2019· book· fr· W4214845558 on OpenAlexaboutno aff

Bibliographic record

VenuePresses universitaires de Rennes eBooks · 2019
Typebook
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Au Canada, les selfies du premier ministre Justin Trudeau sont devenus un marqueur de son identité politique et une ressource stratégique. En France, Nicolas Sarkozy, et plus récemment Emmanuel Macron, ont multiplié les couvertures de Paris Match, accédant avant même d’être élus au statut de célébrités politiques, n’hésitant pas à jouer sur les ressorts de la peopolisation pour asseoir leur visibilité et leur légitimité. Entre scandalisation et médiatisation promotionnelle, une nouvelle économie politique de la célébrité s’est imposée aux leaders politiques, désormais soumis à ces « tyrannies de l’intimité » dont parlait déjà Richard Senett à la fin des années 1970, comme au panoptisme des réseaux sociaux. En croisant les analyses et les regards transatlantiques, en confrontant les trajectoires – celles de Louise Michel et de Rachida Dati, de Marine Le Pen et de sa nièce Marion Maréchal Le Pen, d’Emmanuel Macron et de Justin Trudeau – il s’agit alors de tenter comprendre ce que la culture de la célébrité fait à la politique. Dévoiement de la politique pour les uns, appauvrissement du débat, disqualification du discours au profit des logiques émotionnelles, danger de démagogie par l’hypertrophie des affects, propension à l’exhibitionnisme des prétendants et au voyeurisme des électeurs, l’irruption de la « topique de la célébrité » peut aussi être considérée comme un outil de revitalisation de la politique à l’heure du désenchantement démocratique et de la crise de la représentation.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: yes
Not applicablelow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.780
Threshold uncertainty score0.800

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.001
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.4390.197

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 head0.016
GPT teacher head0.210
Teacher spread0.194 · 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

Labeled directly by 2 models reading the full record.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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