Let me inform you how to tell a convincing story: CBCA and reality monitoring scores as a function of age, coaching, and deception.
Bibliographic record
Abstract
The first aim of this experiment was to examine whether being informed about a method of detecting deception called Criteria-Based Content Analysis (CBCA) would increase participants' CBCA scores when deceptive so that they might then be classified as truthful.The second aim was to investigate whether Reality Monitoring could be used as an alternative tool for verbal lie detection.The third aim was to examine whether participants' social skills (social anxiety, self monitoring and social adroitness) affected their CBCA scores.[11][12][14][15] and undergraduates) participated in a "rubbing the blackboard" event.In a subsequent interview they told the truth or lied about the event, after they were or were not taught some CBCA criteria.Truth tellers obtained higher CBCA scores than liars, and those who were informed about CBCA obtained higher scores than those who were not, except for the 6-8-year-olds.CBCA scores were also significantly correlated with social skills.Finally, Reality Monitoring was a useful alternative to CBCA for distinguishing between liars and truth tellers.Let Me Inform You How to Tell a Convincing Story: CBCA and Reality Monitoring Scores as a Function of Age, Coaching and DeceptionTo date, Criteria-Based Content Analysis (CBCA) -a systematic assessment of the credibility of written statements-is probably the most popular instrument to assess the veracity of written statements (Vrij, 2000).CBCA is a systematic assessment of the credibility of written statements.Steller and Khnken (1989) compiled a list of 19 criteria which had been used in such assessments.CBCA is based on the hypothesis, originally stated by Undeutsch (1967), that a statement derived from memory of an actual experience differs in content and quality from a statement based on invention or fantasy.This is known as the Undeutsch Hypothesis (Steller, 1989).The presence of each criterion strengthens the hypothesis that the account is based on genuine personal experience.Khnken (1989Khnken ( , 1996Khnken ( , 1999Khnken ( , 2002) ) presented theoretical support for the Undeutsch hypothesis and proposed that both cognitive and motivational factors influence CBCA scores.With regard to cognitive factors, it is assumed that, compared to those who fabricate a story, someone who actually experienced an event would be able to produce descriptions about this event which include more CBCA criteria, as some criteria (unstructured production, contextual embedding, reproduction of speech, unusual details, etc.) are believed to be very difficult for people to fabricate.Other criteria are more likely to occur in truthful statements for motivational reasons.Truthful persons will not be as much concerned with impression management as will deceivers.Compared to truth tellers, deceivers would be more keen to try to construct a report which they believe will make a credible impression on others, and will leave out information which, in their view, will damage their image of being a truthful person (Khnken, 1999).As a result, a truthful person's statement is more likely to contain information that is inconsistent with the beliefs/stereotypes that people have concerning truth
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".