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Record W3026808380 · doi:10.1177/0887403420921443

Parental Responsibility, Blameworthiness, and Bullying: Parenting Style and Adolescents’ Experiences With Traditional Bullying and Cyberbullying

2020· article· en· W3026808380 on OpenAlexafffundabout
Ryan Broll, Dylan Reynolds

Bibliographic record

VenueCriminal Justice Policy Review · 2020
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Guelph
FundersLupina Foundation
KeywordsPsychologyParenting stylesDevelopmental psychologyStyle (visual arts)Human factors and ergonomicsSocial psychologyPoison control

Abstract

fetched live from OpenAlex

Parents are deemed morally—and, increasingly, legally—responsible for their children’s misbehavior, and their parental aptitude is questioned if their children are victimized. Parental responsibility laws and blameworthiness extend to common occurrences like bullying. Literature broadly supports these principles for some offenses through findings that effective parenting styles are associated with improved adolescent outcomes, but evidence about the relationship between parenting styles and bullying is underdeveloped and inconclusive. To study the relationship between parenting styles and traditional bullying and cyberbullying offending and victimization, data were collected from a sample of 435 Canadian middle and high school students. The results suggest that parenting styles are not associated with traditional bullying offending or victimization; however, neglectful parenting was associated with cyberbullying offending and indulgent parenting was associated with cyberbullying victimization. These findings suggest that the demandingness dimension of parenting, which is characterized by rule setting and monitoring, is important for cyberbullying prevention.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.426
Threshold uncertainty score0.847

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.077
GPT teacher head0.344
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2020
Admission routes3
Has abstractyes

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