Mitigating Distress and Hate: A Rapid School-based Response to the Israeli-Palestinian Conflict in Quebec, Canada
Bibliographic record
Abstract
Objectives: Since October 2023, the Israeli-Palestinian conflict has particularly affected international communities and diasporas. Within Quebec, Canada, these tensions added to existing social polarization, not sparing the school environment, by creating feelings of fear, anger, powerlessness, deteriorating school climate, and hindering individuals’ and groups’ ability to empathize with one another. This article reports a rapid intervention aimed at mitigating distress and hate in the educational environment through training and supporting school teams. Methods: Five training webinars were organized for school professionals within the Quebec Ministry of Education. Pre-webinar surveys were disseminated to participants to identify if and how the conflict had impacted their school environment to inform trainings. A thematic analysis was carried out on pre-webinar survey responses, chat feedback and field notes collected throughout the webinars. Results: Having an opportunity to share and address concerns, receive reassurance, positive reinforcement, and guidance around strategies, proved to be helpful for school teams. In a context of crisis and politicized emotions, the intervention legitimized a range of emotional responses, addressed school team and community divides, and encouraged double empathy while acknowledging its limits. Finally, these activities also unveiled the potential dangers associated with silencing dissent and highlighted the value of mobilizing agency around school teams’ common mandate to educate and protect children from all communities, and in spite of the expression of divergent solidarities. Implications: In times of crisis, school team and youth engagement in empathy-based activities facilitating non-judgmental expression and awareness of the Other can appease heightened emotions and prepare for dialogue, healing, and coexistence as a way forward.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".