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Record W4401815426 · doi:10.1080/09362835.2024.2390063

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)

2024· article· en· W4401815426 on OpenAlexaff
Sarah J. Macoun, Laurissa Evancio, Chad A. Rose, Todd Milford

Bibliographic record

VenueExceptionality · 2024
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPsychologySocial connectednessHarmApplied psychologyHuman factors and ergonomicsPoison controlSuicide preventionMedical educationDevelopmental psychologyClinical psychologySocial psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.351
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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