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Record W2613631263

A Study of the Dynamic Between Victims and Teachers: How Teachers Can Support Students Who Are Bullied

2017· article· en· W2613631263 on OpenAlexaboutno aff
Jamie S. McLean

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyPedagogy
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this qualitative research study was to investigate the main research question posed: How is a sample of elementary teachers providing one-on-one support to students who are bullied? Data was collected from two Ontario certified teachers, by using semi- structured interviews. The data was analyzed and interpreted, which allowed three main themes to emerge: Teachers acknowledge that how they support students who are bullied is influenced by how they themselves define bullying, recognize it and what it means to them; teachers recognize the importance of implementing different strategies and approaches in the classroom in order to prevent bullying incents; and teachers recognize the need for victims of bullying to be supported through a variety of strategies. I explain the implications of my research findings for the educational community and myself, as a researcher and teacher. The findings inform school boards, principals and teachers about anti-bullying initiatives, which can be implemented in order to help support victims of bullying.

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)0.009
metaresearch head score (Gemma)0.024
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 score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0170.014
Scholarly communication0.0070.006
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.302
Teacher spread0.279 · 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
Published2017
Admission routes1
Has abstractyes

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