Criteria‐based content analysis of true and suggested accounts of events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it