Not every story has two sides: the effect of false balance on perceived scientific consensus about interrogation practices
Bibliographic record
Abstract
Purpose This study aims to test the effect of a falsely balanced message (i.e. exposure to two opposing arguments) on perceived expert consensus about an interrogation practice. Design/methodology/approach Participants (N = 254) read a statement about minimization tactics and were assigned randomly to one of four conditions, where true expert consensus about the tactic was either presented as high or low, and a balanced message (i.e. read two opposing arguments about the factual nature of the tactic) was present or absent. Findings Results showed that exposure to balanced messages led to less perceived expert consensus; especially when true expert consensus about the tactic was high. Exposure to balanced messages also reduced public support for experts testifying about the interrogation tactic. Research limitations/implications Such findings suggest that pairing expert knowledge (i.e. empirical evidence) about investigative interviewing issues with denials might be powerful enough to override scientific beliefs about important matters in this field. Originality/value Researchers in the field of investigative interviewing have put much effort into developing evidence-based interviewing practices and debunking misconceptions on the field. While knowledge mobilization is particularly important in this consequential, applied domain, there are some individuals who aim to hinder the advancement and reform of investigative interviewing. Falsely balancing scientific findings (e.g. minimization tactics imply leniency) with denials is but one of many practices that can distort the public’s perception of expert consensus on an issue. It is crucial for investigative interviewing researchers to recognize such strategies and develop ways to combat science denialism.
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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 | 0.074 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".