Look both ways before crossing: Using a triangulation of art-based methods to transform student – staff relationships as it relates to school climate
Bibliographic record
Abstract
By engaging in an art(s) based action-research case study, youth from a Montréal school considered student-staff relationships as they relate to school climate. Three arts-based data collection methods (timeline, relational map, and photovoice) were used to gain awareness on how relationships with staff constituted the primary determinant of student behavior, student engagement, and a robust school climate. Positive relationships were found to be facilitated by staff who authentically engage with students’ lives outside the classroom, demonstrate equity and discretion in classroom management practices, and allow for redemption following rule infractions or conflicts.With the goal of enacting sustainable change in the school environment, participants collaborated in drafting a Call to Action addressed to the school’s administration, advocating for the creation of a student council as a space to voice their positions, build better communication with staff, and foster a healthy school climate. The paper thus illustrates how art(s) based action-research can contribute to transforming school environments.
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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 | 0.078 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".