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Record W4415012663 · doi:10.1177/07435584251380799

Adolescents’ Viewpoints on Fair and Unfair Ways to Address Peer Harm in Schools

2025· article· en· W4415012663 on OpenAlexafffund
Laura Pareja Conto, Holly Recchia, Ana María Velásquez, Vilma Escorcia Vera, Gabriel Vélez, Cecilia Wainryb

Bibliographic record

VenueJournal of Adolescent Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of CanadaConcordia University
KeywordsHarmViewpointsFocus groupThematic analysisConstruct (python library)Peer groupReflexivityQualitative research

Abstract

fetched live from OpenAlex

This study sought to explore what students thought were fair and unfair approaches to addressing peer harm in their school, and how they construct these understandings through their lived experiences. Participants were 33 adolescents (18 girls, 14 boys, 1 other not specified gender; M age = 14.79, SD = 0.90) attending ninth grade in a public urban school in Bogotá, Colombia. Most adolescents were born in Colombia (91%), followed by Venezuela (9%). In individual interviews, adolescents were asked to narrate two experiences of peer harm at school, one that in their view had been handled fairly by teachers or other school staff, and another that they thought had been handled unfairly. Then, each youth participated in one focus group dialog with other participants, where they discussed fair responses to address peer harm in school. Using reflexive thematic analysis, we developed four themes: responses were judged as fair depending on whether they (1) involved an intervention, (2) were thorough and nuanced, (3) were aligned with goals in the aftermath of harm, and (4) centered students’ needs. These themes showcased the different considerations that constitute youths’ understandings of fairness, which reflected varying concerns related to retributive, distributive, procedural, and restorative justice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.303
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.196
GPT teacher head0.521
Teacher spread0.326 · 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 teacher head, 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

Citations0
Published2025
Admission routes2
Has abstractyes

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