If They Talk, Will You Listen? How Youth Perspectives Should Inform Behavior Management Practices Towards Sustainable School Climate Improvement
Bibliographic record
Abstract
Student behavior management remains a pivotal area of consideration for pre- and in-service secondary teachers and administrators alike. Hoping to gain deeper insight into the intersection between student behavior and school climate an action research methodology is employed to invite students into the debate as active school stakeholders. A case study from one inner city high school engaged 25 youth participants in a 6-week long focus group to learn about the research process through the creation and analysis of three arts-based data collection methods: timeline, relational map, and photovoice. Student led analysis of the data demonstrated that student behavior is inextricably linked to the quality of the social aspect of school climate. Healthy school climates which inspire positive student engagement and behavior are ones which: 1) foster positive student-staff relationships; 2) incorporate pedagogical practices which allow for student voice and choice; and 3) encourage the opportunity for redemption following infractions. While restorative measures are presented as effective ways to promote positive student behavior and healthy school climates, stakeholders wishing to authentically transform school systems are cautioned to reflect deeply on power and privilege to dismantle traditional structures for sustainable growth.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".