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Record W2346601514 · doi:10.4000/vertigo.17115

Parasismique, oser éprouver l’acceptabilité sociale

2016· article· fr· W2346601514 on OpenAlexvenueno aff
Stéphane Cartier, Cloé Vallette

Bibliographic record

VenueVertigO · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

La confrontation aux risques naturels provoque une réorganisation sociale constante. L’acceptation de la menace et l’acceptabilité des solutions pour réduire la vulnérabilité évoluent avec les connaissances des phénomènes et le consentement collectif à la contrainte. En France, l’importance de l’engagement étatique face aux risques naturels dissuade d’en interroger l’acceptabilité collective. La protection parasismique de l’habitat impose un zonage de l’aléa sismique pour graduer les contraintes architecturales selon la probabilité de secousses. La révision probabiliste de ce zonage est l’occasion d’interroger sa doctrine et sa matrice scientifique pour comprendre les choix réglementaires opérés par les experts et les pouvoirs publics. En contraste, l’enquête sociologique REVDOU effectuée en 2011 à Salon-de-Provence, Albertville, Mulhouse et Tarbes, sur la protection parasismique par les propriétaires, constructeurs et administrations montre comment l’acceptabilité du risque varie selon le consensus local autour de l’organisation des solutions préventives. Pour ces quatre situations de sismicité moyenne, le renouvellement du zonage sismique réglementaire réinterroge les relations autour du contrôle collectif de la solidité des constructions. L’article montre l’écart entre conception probabiliste de l’aléa à l’origine de la réglementation et l’acceptabilité locale du risque sismique et des contraintes professionnelles entre acteurs pour diminuer un danger invisible.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.664
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.003

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 head0.035
GPT teacher head0.263
Teacher spread0.229 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2016
Admission routes1
Has abstractyes

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