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Record W3181510902 · doi:10.1017/9781108776684.010

The Importance of a Positive School Climate in Addressing Youth Retaliation

2021· book-chapter· en· W3181510902 on OpenAlexaff
Allison Ann Payne, Denise Wilson

Bibliographic record

VenueCambridge University Press eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsConcordia University
Fundersnot available
KeywordsHarmSchool climateSchool disciplineRestorative justicePsychological interventionNormativePsychologyJuvenile delinquencyRetributive justiceCriminologySocial psychologyEconomic JusticePolitical sciencePedagogy

Abstract

fetched live from OpenAlex

Authoritarian and retributive discipline policies that characterize the crime control model of school discipline feed into a cycle of harm whereby aggressive behaviors, including retribution and revenge, are likely to be reinforced as normative responses to an unjust and unequal environment. These policies are ineffective at reducing violence and delinquency, can result in poor academic outcomes, are disproportionately applied, and have negative impacts on the school climate. They also fail to capture the importance of teaching students the social and behavioral skills that are associated with better academic and behavioral outcomes. By contrast, the school climate is an important malleable component that can have positive impacts on a variety of outcomes. Whole school approaches to positive discipline such as Positive Behavioral Interventions and Supports and Restorative Justice are far more promising policies to improving the school climate and associated student outcomes, including retaliatory behavior.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.071
GPT teacher head0.304
Teacher spread0.234 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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