Are Experts (News)Worthy? Balance, Conflict, and Mass Media Coverage of Expert Consensus
Bibliographic record
Abstract
Overlooked in analyses of why the public often rejects expert consensus is the role of the news media. News coverage of expert consensus on general matters of policy is likely limited as a result of journalists’ emphasis in news production on novelty and drama at the expense of thematic context. News content is also biased towards balance and conflict, which may dilute the persuasiveness of expert consensus. This study presents an automated and manual analysis of over 280,000 news stories on ten issues where there are important elements of agreement among scientists or economists. The analyses show that news content typically emphasizes arguments aligned with positions of expert consensus, rather than providing balance, and only occasionally cites contrarian experts. More troubling is that expert messages related to important areas of agreement are infrequent in news content, and cues signaling the existence of consensus are rarer still.
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 | 0.018 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".