Promotional Language (Hype) in Abstracts of Publications of National Institutes of Health–Funded Research, 1985-2020
Notice bibliographique
Résumé
Importance: Investigators applying for National Institutes of Health (NIH) funding increasingly use promotional language (or hype) that has the potential to undermine objective evaluation. Whether or not the same investigators use hype in subsequent research reports has yet to be investigated. Objective: To assess changes in the use of hype in journal abstracts reporting research funded by the NIH and to compare those trends with previously reported trends in the associated NIH funding applications. Design, Setting, and Participants: This cross-sectional study assessed trends (from 1985 to 2020) in the use of promotional adjectives in abstracts of journal articles reporting NIH-funded research, and then compared those trends with previously reported trends for the associated NIH funding applications. Articles included in analyses had abstracts available in PubMed. Main Outcomes and Measures: Absolute change for the 139 adjective forms that have previously been identified as representing hype in NIH funding applications was measured as the difference in frequency between 1985 and 2020. Relative change was measured as the percentage change in frequency in 2020 relative to 1985, or the first year of occurrence. Consistency of change was measured by the rank order correlation (Kendall τ). Concordance between longitudinal trends in the journal abstracts and NIH funding applications was measured by the rank-order cross-correlation. Results: In a total of 2 394 480 journal abstracts, all 139 adjective forms were identified in 2 793 592 total occurrences. Among these adjectives, 133 increased in absolute frequency by 5335 words per million (wpm), with a mean (SD) relative increase of 1404% (2371%). The largest absolute increases were for novel (524 wpm), important (414 wpm), and key (378 wpm). The largest relative increases were for scalable (22 wpm [19 964%]), unmet (23 wpm [12 126%]), and tailored (40 wpm [8169%]). The mean (SD) correlation for all adjectives was 0.70 (0.30) with 95 adjectives showing a strong positive correlation (τ > 0.7; P < .001), 24 a moderate positive correlation (0.5 < τ < 0.7; P < .001), and 3 a moderate negative correlation (-0.5 < τ < -0.7; P < .001). The mean (SD) cross-correlation was 0.64 (0.19) with 61 of the 139 adjectives showing a strong positive cross-correlations (τ > 0.7; P < .001), 53 a moderate positive cross-correlations (0.5 < τ < 0.7; P < .001), and 3 a moderate negative cross-correlation (-0.7 < τ < -0.5; P < .001). Conclusions and Relevance: In this analysis of journal abstracts reporting NIH-funded research from 1985 to 2020, levels of promotional language were found to be increasing and trends were closely associated with previously reported trends in the related NIH funding applications. This suggests that increasing levels of salesmanship may in part be a downstream effect of salesmanship infused during earlier stages of the research cascade.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».