‘How to Become a Part Without Falling Apart?’ \nIntroducing Creative Activities in The Pedagogy of Ten 'Welcome Class' Elementary School \nTeachers in Montreal, Quebec
Bibliographic record
Abstract
This educational action research aimed to explore how applied theatre and visual \nactivities can be tailored to meet the needs of Welcome Class teachers' pedagogy and be easily \nimplemented. The collaborative research process involved introducing ten 'd’accueil' Montreal \npublic elementary school teachers to a wide selection of drama games and performative and \nvisual activities during two research workshop sessions in the spring of 2024. To improve \ncommunication and understand the socioemotional and linguistic needs of their immigrant, \nrefugee, and asylum seeker students, the teachers applied new activities in their classrooms \nbetween the first and second workshop sessions. They maintained an 'Activity Journal' to \ndocument their personal process of facilitating, noting students' reactions and outcomes, and \nreflecting by sharing with the researcher and their peers and offering modifications to the \nactivities. This collaborative method enabled and encouraged participants to share experiences, \nsupport each other with resources, and enrich their curricula. \nThe research outcomes revealed constant struggles teachers encounter, such as a lack of \ncommunication with families due to linguistic and cultural differences related behaviours, pre- \nand post-migration traumatic outbursts, and a lack of personal support from their institutions.
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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".