Supporting young children's minoritized languages in a community setting: insights from community-based participatory research
Bibliographic record
Abstract
Community-Based Participatory Research is a research approach specifically designed to reduce power imbalances when working with marginalised communities towards advancing social justice. We pair this approach with Transformative Research Paradigm that aims to include marginalised voices in the research process. This paper reports on a study that incorporated a Community-Based Participatory Research approach and Transformative Research paradigm to support and enhance minoritized language transmission and maintenance among preschool-aged children and their parents. Within the Canadian context, few supports exist for minoritized home languages in the mainstream community. It is within this context, our partnership implemented a multilingual language group for parents and children who shared minoritized languages. This paper reports on the process of implementing this Community-Based Participatory Research including (1) the formation of the partnership, (2) the assessment of the community strengths and dynamics, the identification of priority concerns and research questions, (4) the design and conduct of the pilot implementation, (5) the gathering of feedback and interpretation of research findings, and (6) the dissemination and knowledge translation. The goal of this paper is to contribute a better understanding of how Community-Based Participatory Research can be applied to support partnerships with community members towards enhancing multilingual language development.
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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 | 0.040 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".