Truthiness and law: <scp>Nonprobative</scp> photos bias perceived credibility in forensic contexts
Bibliographic record
Abstract
Summary Nonprobative but related photos can increase the perceived truth value of statements relative to when no photo is presented ( truthiness ). In two experiments, we tested whether truthiness generalizes to credibility judgments in a forensic context. Participants read short vignettes in which a witness viewed an offence. The vignettes were presented with or without a nonprobative, but related photo. In both experiments, participants gave higher witness credibility ratings to photo‐present vignettes compared to photo‐absent vignettes. In Experiment 2, half the vignettes included additional nonprobative information in the form of text. We replicated the photo presence effect in Experiment 2, but the nonprobative text did not significantly alter witness credibility. The results suggest that nonprobative photos can increase the perceived credibility of witnesses in legal contexts.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".