Adolescents’ Viewpoints on Fair and Unfair Ways to Address Peer Harm in Schools
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".