Development and Piloting of an Abbreviated Bullying Assessment Tool for Youth with and without Special Education Needs: The Adolescent Risk and Connectedness Survey and Interview (ARC-S and ARC- I)
Bibliographic record
Abstract
Bullying has deleterious effects on the health of youth, families, schools, and communities. These effects are especially pronounced for youth with special education needs specific to emotional and behavioral disorders (EBD). Much of what is understood concerning the effects of bullying has been limited to face-to-face contexts. Despite the ubiquity of technology use among youth and the potential for harm, less is known about cyber-bullying. This study sought to investigate the initial use of a newly developed tool, the Adolescent Risk and Connectedness – Survey and Interview (ARC-S and ARCS- I), to investigate experiences with face-to-face bullying and cyber-bullying, alongside risk and protective factors in youth with and without EBD. The ARC is an ecological systems theory-informed survey and interview tool developed to address some limitations of existing assessment approaches. Preliminary data suggest that ARC is an appropriate tool for adolescents with and without EBD as it provides a holistic and contextualized measure of risk and protective factors associated with bullying in school contexts.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".