Youth-voice driven after-school science clubs: A tool to develop new alliances in ethnically diverse communities in support of transformative learning for preservice teachers and youth
Bibliographic record
Abstract
In this paper, we draw on data collected in the context of a three-year action research project that involved the development of after-school science clubs in three high schools in ethnically diverse communities, made possible through a partnership between a university, the schools and the community. We document the evolution of a youth-voice driven science club over time and the kind of transformative learning it supported for youth who are for the most part first-generation immigrants growing up in an underserved urban centre. We also explore how the alliance between the university, the school and the community enriched the learning ecologies of the participating youth and how it was experienced by the instructors and preservice teachers who pursued service learning projects in the clubs as part of their university course work in education. We show how such diverse experiences offer rich insights into ways of building alliances among schools, community resources and the university to support equity-driven practices that are inclusive and supportive of ethnically diverse youth with complex immigration histories.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".