Can journalistic “false balance” distort public perception of consensus in expert opinion?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Media critics have expressed concern that journalistic "false balance" can distort the public's perceptions of what ought to be noncontroversial subjects (e.g., climate change). I report several experiments testing the influence of presenting conflicting comments from 2 experts who disagree on an issue (balance condition) in addition to a complete count of the number of experts on a panel who favor either side. Compared with a control condition, who received only the complete count, participants in the balance condition gave ratings of the perceived agreement among the experts that did not discriminate as clearly between issues with and without strong expert consensus. Participants in the balance condition also perceived less agreement among the experts in general, and were less likely to think that there was enough agreement among experts on the high-consensus issues to guide government policy. Evidently, "false balance" can distort perceptions of expert opinion even when participants would seem to have all the information needed to correct for its influence.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it