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Record W4399820487 · doi:10.1515/9782760524712

Médias, médicaments et espace public

2009· book· fr· W4399820487 on OpenAlexaboutno aff
Christine Thoër, Bertrand Lebouché, Joseph J. Lévy, Vittorio A. sironi

Bibliographic record

VenuePresses de l'Université du Québec eBooks · 2009
Typebook
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPublic spaceEngineering

Abstract

fetched live from OpenAlex

La question des médicaments occupe une place privilégiée dans les médias qui suivent sa trajectoire, depuis la découverte de nouvelles molécules jusqu’à la commercialisation et à la consommation des produits pharmaceutiques. Quels sont les discours et les images des médicaments qui circulent dans les différents espaces médiatiques ? Comment se construisent-ils et quelles sont leur incidence sur les pratiques et les relations entre les acteurs de la chaîne du médicament ? Répondre à ces questions est l’objectif de cet ouvrage collectif, qui s’intéresse aux enjeux que soulève la médiatisation croissante du médicament. Réunissant les contributions d’experts européens, canadiens et brésiliens issus de plusieurs traditions disciplinaires, il traite de la communication pharmaceutique et de son évolution. Il examine la couverture médiatique des découvertes pharmaceutiques et des crises entourant certains médicaments vedettes. Il s’interroge sur le rôle que joue Internet dans la transformation des logiques de production de l’information pharmaceutique et des relations entre les acteurs concernés par les médicaments. Enfin, il analyse différentes stratégies pour promouvoir une meilleure utilisation des médicaments, de la régulation de la publicité au développpement d’outils médiatiques d’éducation des consommateurs.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0260.007

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 head0.073
GPT teacher head0.345
Teacher spread0.272 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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