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Record W2780777679 · doi:10.1177/1059840517748417

The Factors That Influence and Protect Against Power Imbalance in Covert Bullying Among Preadolescent Children at School

2017· article· en· W2780777679 on OpenAlexaff
Helen Nelson, Sharyn Burns, Garth Kendall, Kimberly A. Schonert‐Reichl

Bibliographic record

VenueThe Journal of School Nursing · 2017
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCovertPsychologyDevelopmental psychologyLinguistics

Abstract

fetched live from OpenAlex

In this article, the perceptions of preadolescent children (ages 9-11) regarding factors that influence and protect against power imbalance associated with covert aggression and bullying are explored. In aggression research, the term covert has been typically used to describe relational, indirect, and social acts of aggression that are hidden. These behaviors contrast with overt physical and verbal aggression. Children have previously conveyed their belief that covert aggression is harmful because adults do not see it even though children, themselves, are aware. We used focus groups to explore children's understanding of covert aggression and to identify children's experience and perception of adult support in relation to bullying. Thematic analysis supported the definition of covert aggression as that which is intentionally hidden from adults. Friendship, social exclusion, and secret from teacher were identified as factors that influence power imbalance, while support from friends and adult support protected against power imbalance.

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)0.003
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.016
GPT teacher head0.294
Teacher spread0.278 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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