Why Bullying Victims are Not Believed: Differentiating Between Childrenâs True and Fabricated Reports of Stressful and Non-stressful Events
Bibliographic record
Abstract
To date, limited research has been conducted to identify differences in children’s truthful and deceptive statements concerning stressful events. The present study uses automated linguistic software to detect linguistic patterns and objectively differentiate between the true and false stressful reports of bullying and non-stressful reports of sports events 7- to 14-year-olds. Results revealed that children displayed different linguistic patterns when reporting true and false stories, and between stressful and non-stressful stories. A discriminant analysis reliably differentiated between true and false stressful and non-stressful stories, though the veracity of non-stressful stories was more accurately classified than stressful stories. Experiment 2 revealed that adults were below chance levels in accurately identifying children’s true and false reports of stressful events (bullying), with confidence ratings and experience with children failing to improve accuracy scores. Taken together, results reveal that children are able to fabricate emotional and stressful stories that closely replicate their true reports.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".