Science at the supermarket: A comparison of what appears in the popular press, experts' advice to readers, and what students want to know
Bibliographic record
Abstract
The popular print media constitute a major source of new information about scientific research for the public and for members of the scientific community outside their areas of expertise. Despite the potential importance of media reports to scientific literacy and public awareness of science, little is known about the content of these articles. We sampled the popular print media (e.g., publications such as those sold at a convenience store or supermarket) and found that the majority of articles about scientific research were in the form of news briefs. We analyzed and compared (a) the content of these news briefs, (b) advice given by experts about how to read media reports about science critically, and (c) university students' requests for information as they evaluated brief reports about research. Some marked discrepancies were found. For example, much of the information that experts advised readers to attend to or that students spontaneously requested were infrequently available in news briefs. Our findings have implications for conceptualizing scientific literacy, as well as for changing science journalism and science education in ways that can enable readers to become effective consumers of scientific information.
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 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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".