Criteria‐based content analysis of true and suggested accounts of events
Bibliographic record
Abstract
Abstract Worldwide, the criteria‐based content analysis (CBCA) is probably the most widely used veracity assessment technique for discriminating between accounts of true and fabricated events. In this study, two experiments examined the effectiveness of the CBCA for discriminating between accounts of true events and suggested events believed to be true. In Experiment 1, CBCA‐trained judges evaluated participants' accounts of true and suggestively planted childhood events. In Experiment 2, judges analysed accounts of recent events that were experimentally manipulated to be a (a) true experience, (b) false experience believed to be true and (c) deliberately fabricated experience. In both experiments CBCA scores were significantly higher for accounts of true events than suggested events. However, this difference was not significant for participants classified as experiencing ‘full’ memories for the suggested event. Self‐report memory measures supported the findings of the CBCA analyses. Taken together these results suggest that the CBCA discriminative power is greatly constrained. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.011 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".