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Record W4409971529 · doi:10.1017/bpp.2025.5

Are media reports of published research an accurate representation of the research?

2025· article· en· W4409971529 on OpenAlexaff
Catherine Yeung, Dilip Soman

Bibliographic record

VenueBehavioural Public Policy · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRepresentation (politics)Computer scienceData scienceInformation retrievalPolitical science

Abstract

fetched live from OpenAlex

Abstract It is perhaps uncontroversial to claim that behavioral science research is playing an increasingly important role in practice. However, practitioners largely rely on media reports rather than original research articles to learn about the science. Do these media reports contain all the information needed to understand the nuances of the research? To assess this question, we develop a set of rubrics to evaluate the fidelity of the media report to the original research article. As an illustration, we apply these rubrics to a sample of media reports based on several research articles published in one journal and identify common patterns, trends, and pitfalls in media presentations. We find preliminary evidence of low fidelity in presenting participant characteristics, contextual elements, and limitations of the original research. The media also appear to misreport correlational evidence as causal and sometimes miss acknowledging the hypothetical nature of evidence when hypothetical scenarios were used as the sole basis of conclusions. Furthermore, the media often present broad conclusions and personal opinions as directly backed by scientific evidence. To support more discerning consumption of behavioral insights from media sources, we propose a checklist to guide practitioners in evaluating and using information from media sources.

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)0.004
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.148
GPT teacher head0.445
Teacher spread0.297 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes1
Has abstractyes

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