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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".