Promotional Language (Hype) in Abstracts of Publications of National Institutes of Health–Funded Research, 1985-2020
Bibliographic record
Abstract
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".