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Record W3155848101 · doi:10.3389/fpsyg.2021.661724

Looking Beyond Assumptions to Understand Relationship Dynamics in Bullying

2021· article· en· W3155848101 on OpenAlexafffundabout
Faye Mishna, Arija Birze, Andrea Greenblatt, Debra Pepler

Bibliographic record

VenueFrontiers in Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsYork UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyDynamics (music)Intervention (counseling)PhenomenonSocial psychologyDevelopmental psychologyPedagogy

Abstract

fetched live from OpenAlex

To account for the complex relationships and processes that constitute the phenomenon of bullying, it is critical to understand how students and their parents and teachers conceptualize traditional and cyberbullying. Qualitative data were drawn from a mixed methods longitudinal study on cyberbullying. Semi-structured interviews were held with Canadian students in grades 4, 7, and 10 in a large urban school board, and their parents and teachers. To account for the complexity and interactions of different systems of relationships, the purpose of the current article is to examine how students and their matched parents and teachers understand traditional and cyberbullying. Central to participants' understanding of traditional and cyberbullying was whether they considered bullying to represent harmful relationship dynamics. Three main assumptions emerged as shaping participants' understanding of bullying and appeared to obscure the deep relationship processes in bullying: (a) assumptions of gender in bullying, (b) type of bullying-comparing traditional and cyberbullying, and (c) physical bullying as disconnected from relationship dynamics. It is essential that assessment, education, and prevention and intervention strategies in traditional and cyberbullying be informed by the inherent relationships in bullying and be implemented at multiple levels of relationships and broader social systems.

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)0.000
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: none
Teacher disagreement score0.740
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.334
Teacher spread0.303 · 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

Citations7
Published2021
Admission routes3
Has abstractyes

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