Truthiness and falsiness of trivia claims depend on judgmental contexts.
Bibliographic record
Abstract
When people rapidly judge the truth of claims presented with or without related but nonprobative photos, the photos tend to inflate the subjective truth of those claims--a "truthiness" effect (Newman et al., 2012). For example, people more often judged the claim "Macadamia nuts are in the same evolutionary family as peaches" to be true when the claim appeared with a photo of a bowl of macadamia nuts than when it appeared alone. We report several replications of that effect and 3 qualitatively new findings: (a) in a within-subjects design, when people judged claims paired with a mix of related, unrelated, or no photos, related photos produced truthiness but unrelated photos had no significant effect relative to no photos; (b) in a mixed design, when people judged claims paired with related (or unrelated) and no photos, related photos produced truthiness and unrelated photos produced "falseness;" and (c) in a fully between design, when people judged claims paired with either related, unrelated, or no photos, neither truthiness nor falsiness occurred. Our results suggest that photos influence people's judgments when a discrepancy arises in the expected ease of processing, and also support a mechanism in which-against a backdrop of an expected standard-related photos help people generate pseudoevidence to support claims.
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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 | 0.005 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".