Philosophy for Children in ethnoculturally diverse schools: Some opportunities and risks of learning about diversity through philosophical dialogue
Bibliographic record
Abstract
First- and second-generation immigrant students make up a signification proportion of the primary and secondary school population on the island of Montreal (Quebec, Canada). The Quebec Ministry of Education’s Policy Statement on Educational Integration and Intercultural Education highlights the importance of teaching students about diversity; Philosophy for Children (P4C) could be a promising approach in this regard. However, some scholars have raised concerns about P4C’s lack of sensitivity regarding racism and marginalisation. This article, drawing on empirical data collected during a larger study, explores some ways in which P4C can help students learn about diversity, as well as some of the risks and limitations of using philosophical dialogue to explore ethnocultural diversity, racism, or immigration. The study, conducted in an ethnoculturally diverse grade four classroom in Montreal between January and June 2023, involved a series of philosophical dialogues with the participating students, as well as semi-structured interviews with the students and with their teacher. The results of the study suggest that P4C can help students gain greater awareness of diversity, explore diversity from a theoretical perspective, and question certain stereotypes through collective reflection. However, they also point to certain risks inherent in using P4C to discuss diversity, highlighting the importance of careful facilitation and increased sensitivity training for P4C facilitators around the experiences of immigrant and racialised students. I conclude by proposing some reflections both for facilitators and for future academic research in the field.
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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 | 0.074 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.067 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".