Fore! Does forewarning inoculate people against the false balance effect?
Bibliographic record
Abstract
Abstract Background & Aims We examined the effect of falsely balanced messages on perceptions of expert consensus about non‐verbal lie detection and whether forewarning inoculates people against the fake debate strategy. Materials & Methods Participants ( N = 307) read a media report that revealed high consensus among experts (nearly 90%) that non‐verbal cues are unreliable indicators of deception and were randomly exposed to (1) no comments from experts, (2) balanced comments (three comments from each expert on opposing sides), (3) evidentiary balanced comments (five comments from a deception detection expert and one comment from a contrarian expert), (4) balanced comments along with a forewarning about the ‘fake debate’ strategy, or (5) evidentiary balanced comments along with a forewarning about the ‘fake debate’ strategy. Results Results showed that participants intuitively believe that non‐verbal cues are reliable indicators of deceit. Although participants were made aware that the consensus from scientists is that non‐verbal lie detection is futile, the inclusion of balanced comments alongside the data still decreased perceived scientific consensus. Balanced comments also reduced people's policy support in favour of scientific consensus, and forewarning had minimal effect. Discussion We discuss the implications of our findings for efforts to mitigate the fake debate strategy.
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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 | 0.017 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".