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Enregistrement W195741156

Bending Science: How Special Interests Corrupt Public Health Research

2008· article· en· W195741156 sur OpenAlexaboutno aff
Jennifer Sass

Notice bibliographique

RevuePubMed Central · 2008
Typearticle
Langueen
DomaineMedicine
ThématiqueScience, Research, and Medicine
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHarmPublic relationsProduct (mathematics)Political scienceBusinessLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Biased reporting of science has been documented for industry-supported research on many hazardous substances, including the plasticizer bisphenol A, secondhand tobacco smoke, asbestos, and lead. Several books that have hit the stands recently (e.g., David Michaels’ Doubt Is Their Product) use case studies to document and discuss the effect this kind of bias has on public health and environmental protection. In Bending Science McGarity and Wagner discuss the methods and motivations that make this practice so pervasive. The book could be called “Idiot’s Guide to Bending Science” because its chapters neatly and logically provide a step-by-step plan for manipulating science to support a predetermined conclusion. Starting with who has an interest in the manipulation of science, the book describes how to distort science without getting caught, how to support “bent” science by attacking legitimate science and scientists, and finally how to use public relations firms and journalists to advertise and disseminate the “bent” science. In addition to “how,” the book tells us why manufacturers and other financially interested parties are motivated to manipulate science—namely, to weaken the regulation of their products and to defend themselves in litigation if harm comes from their products. A recent illustration of the impact of “bent science” on public health is evident in the Food and Drug Administration’s (FDA) draft assessment of bisphenol A issued this summer, declaring the chemical was safe as currently used. The FDA’s assessment relied on just two studies, which were funded by the American Chemistry Council (formerly the Chemical Manufacturers Association), Dow Chemical, Bayer, and other plastics manufacturers, and the agency ignored dozens of other studies done by independent scientists that reported evidence of harm. The FDA’s conclusions also conflict with two National Institutes of Health reviews and the actions of its counterpart in Canada. An example of the failure of our regulatory oversight mechanisms to provide a backstop was evident this summer when Congress was compelled to pass legislation to eliminate lead in children’s toys and to ban or temporarily suspend the use of six types of phthalates (components of plastics) in children’s products. Congress stepped in after regulatory agencies failed to take action, even though children had been widely exposed (one child died in March 2006 from lead-contaminated toys) and there was substantial scientific evidence that these chemicals were highly hazardous. Bending Science has a halting academic writing style that overly relies on secondary sources as resources. In addition, the authors argue that everyone bends science, even public health advocates; however, the few public health examples that the authors provide are relatively rare instances that do not support those sweeping conclusions. For example, a case study of plaintiffs’ lawyers artificially inflating silicosis cases fails to mention that this was a highly unusual instance for which the offending lawyers were issued sanctions for their transgressions. In fact, without trial lawyers much of the evidence that the authors rely on for this book, such as the tobacco industry documents, would have never been released for public scrutiny. This is a topic of great importance. Bending Science warns that when science becomes artificially manipulated to misrepresent the hazards of products, “serious adverse consequences for human health and the environment, as well as for the economic well-being of legitimate businesses,” may arise.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,167
score de la tête « metaresearch » (Gemma)0,206
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,969
Score d'incertitude au seuil0,884

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1670,206
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0040,003
Études des sciences et des technologies0,0220,129
Communication savante0,0390,034
Science ouverte0,0050,021
Intégrité de la recherche0,0310,038
Charge utile insuffisante (le modèle a refusé de juger)0,0060,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,385
Tête enseignante GPT0,429
Écart entre enseignants0,044 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeThéorique ou conceptuel
DomaineMéthodes
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2008
Routes d'admission1
Résumé présentoui

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