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Record W3174705159 · doi:10.82308/42611

Bullying policy and implementation: A case study analysis

2016· article· en· W3174705159 on OpenAlexaboutno aff
Jennifer Bruce

Bibliographic record

VenueeScholarship@McGill (McGill) · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceComputer securityCriminologyPublic relationsComputer sciencePsychology

Abstract

fetched live from OpenAlex

In Canada, researchers estimate that 23.3% of children are involved in some form of bullying (i.e., bully, victim or witness; Craig et al., 2009). Furthermore, the consequences of bullying are severe and often cause serious psychological harm (e.g., anxiety, depression, decreased self-esteem), physical damage (e.g., injury, stomach aches, headaches), and increased suicide risk (e.g., Hawker & Boulton, 2000; Nansel et al., 2001; Reijntjes, Kamphuis, Prinzie, & Telch, 2010; Storch & Ledley, 2005; Wolke, Woods, Bloomfield, & Karstadt, 2001). Despite the implementation of several anti-bullying intervention programs, bullying remains a problem in today's schools.Bullying, by definition involves an imbalance of power and can be considered the result of inequalities among social groups. Additionally, rules and regulations pertaining to bullying (i.e., bullying policies) have the potential to equalize power imbalances and reduce the occurrence of these behaviors in a school. Moreover, in 2012, the government of Quebec implemented Bill 56 for the purpose of preventing and stopping all forms of bullying in schools. This phenomenological case study employed a social justice epistemology to investigate the following research questions: (a) How are bullying policies constructed with regards to definition, purpose of items, underlying assumptions, and accessibility? (b) How are bullying policies understood by school personnel, at varying levels within a single school? (c) How is policy implementation described by school personnel at varying levels within a single school? To this end, data included an analysis of Bill 56, the school's bullying policies as well as online surveys and semi-structured interviews of school personnel. Furthermore, power and privilege were considered at all levels of analysis. Results of this study showed several positive changes in response to the new government guidelines, including a common language and definition with respect to bullying, the removal of victim responsibilities, the inclusion of items concerning confidentiality, and the inclusion of guidelines with respect to witnesses and parents. In addition to several positive changes from the implementation of Bill 56, the analysis revealed continued weaknesses with respect to the written policy in the areas of accessibility, diversity factors, macro-level intervention factors, and the inclusion of adults in the bullying dynamic. As well, there was a relationship between the written policy, participants' understanding of the policy and participants' descriptions of implementation. Implications for school policy development, training, implementation, and school psychologists are discussed as well as recommendations are made for future research.

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)0.015
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0120.003
Scholarly communication0.0040.003
Open science0.0030.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.420
Teacher spread0.363 · 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 designQualitative
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
Published2016
Admission routes1
Has abstractyes

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