Dilemma: Utilizing the activation decision-construction-action theory to understand and predict children's hypothetical decisions to conceal cases of school bullying
Bibliographic record
Abstract
The Decision component of the Activation-Decision-Construction-Action theory (ADCAT) proposes that if people perceive the benefits of lying higher than the truth, they are more likely to lie. To expand on the existing ADCAT research, the current study investigated the cost-benefit appraisals of 115 children ages 7-to-14 when concealing information about school bullying. Further, the current study examined the impact of the type of bullying (verbal vs. physical), type of exposure to bullying (victim vs. bystander-witness), and familiarity of the person to whom they could disclose (familiar adult vs. unfamiliar adult) when evaluating ADCAT. The results indicate that the expected value of lie-telling and motivation to lie were only significantly related to decisions to lie when the child is the victim of physical bullying and being questioned by a familiar person. Whereas the expected value of truth-telling was only significantly related to decisions to lie when the child is the victim of verbal bullying and being questioned by an unfamiliar person. Discriminant function analysis models were also statistically significant for these two vignettes, meaning that ADCAT-dependent measures could be used to accurately classify the truth and lie tellers for these two vignettes. Furthermore, developmental factors such as age, gender and Theory-of-Mind skills of the ADCAT-dependent measures within each scenario were examined. This study provides further understanding of the complexities in cases of school bullying, particularly as it relates to social-cognitive factors that encourage or discourage children and adolescents from disclosing these events.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".