Science writers ’ reactions to a medical ‘‘breakthrough’ ’ story
Bibliographic record
Abstract
In numerous incidences, the news coverage of medical research has incited unjustified optimism or fear. The medical literature provides an archive of the scientific community’s condemnation of these misleading reports, but little is known about how they are judged by newsmakers. This study explored science writers ’ reactions to a controversial New York Times story that inflated the hopes of thousands of cancer patients. More than 60 science writers in the US, Canada, and Great Britain participated in a 12-day email discussion triggered by the Times article. We analyzed 255 of these email postings and coded (1) positive and negative critiques of the Times story, (2) references to the article’s repercussions including the creation of false hope, (3) attributions of responsibility for the resulting public misunderstanding, and (4) suggestions to improve the public’s comprehension of medical research news. The participating science writers generally responded negatively to the controversial article: 83 % of the critiques were unfavorable. In addition, the science writers in the sample were cognizant and concerned about the impact of their work on the public, and accepted the largest share of the responsibility for the false hope created by the news coverage of medical research. Finally, the suggestions offered by respondents to improve the public’s understanding of medical research news were similar to those proposed by the scientific community. Thus, some commonality exists between how
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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".