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Record W2888100790 · doi:10.1177/0829573518793752

Should I Defend or Should I Go? An Adaptive, Qualitative Examination of the Personal Costs and Benefits Associated With Bullying Intervention

2018· article· en· W2888100790 on OpenAlexaff
Natalie Spadafora, Zopito A. Marini, Anthony A. Volk

Bibliographic record

VenueCanadian Journal of School Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsBrock University
Fundersnot available
KeywordsPsychologyIntervention (counseling)Human factors and ergonomicsPoison controlSuicide preventionInjury preventionSocial psychologyApplied psychologyCognitionMedicineMedical emergencyPsychiatry

Abstract

fetched live from OpenAlex

Bystanders play a crucial role in encouraging or preventing bullying situations and feature prominently in several international antibullying programs (e.g., KiVa). Despite a surge of recent interest in bystanders, relatively little is known about the functional reasons why individuals choose to engage with or ignore bullying incidents. Given the importance of bystanders’ influence on bullying, we argue that further consideration needs to be given to the individual costs and benefits of bystanders’ intervention. Adolescents in our study ( N = 101, M = 15.37 years) read different bullying scenarios and were then asked to respond with how the bystander would react in each scenario while considering and explaining potential personal costs and benefits. We focused on the cognitive reasoning of important factors adolescents may consider when faced with the decision of whether to intervene or not in a bullying situation. Our study provides novel evidence that adolescents engage in quite explicit cost–benefit decisions regarding their decisions of whether or not they would intervene in bullying. The content and structure of these cost–benefit decisions support an adaptive model of bullying behavior and may be helpful in developing more targeted peer-based antibullying programs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.142
GPT teacher head0.382
Teacher spread0.241 · 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.

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

Citations57
Published2018
Admission routes1
Has abstractyes

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