Are media reports of published research an accurate representation of the research?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.217 | 0.731 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.023 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".