Modeling the anti‐cyberbullying preferences of university students: Adaptive choice‐based conjoint analysis
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
Adaptive choice-based conjoint analysis was used to study the anti-cyberbullying program preferences of 1,004 university students. More than 60% reported involvement in cyberbullying as witnesses (45.7%), victims (5.7%), perpetrator-victims (4.9%), or perpetrators (4.5%). Men were more likely to report involvement as perpetrators and perpetrator-victims than were women. Students recommended advertisements featuring famous people who emphasized the impact of cyberbullying on victims. They preferred a comprehensive approach teaching skills to prevent cyberbullying, encouraging students to report incidents, enabling anonymous online reporting, and terminating the internet privileges of students involved as perpetrators. Those who cyberbully were least likely, and victims of cyberbullying were most likely, to support an approach combining prevention and consequences. Simulations introducing mandatory reporting, suspensions, or police charges predicted a substantial reduction in the support of uninvolved students, witnesses, victims, and perpetrators.
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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 | 0.001 | 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