Leveraging Languagerich Classrooms As A Resource For Fostering Critical And Creative Global Citizenship
Bibliographic record
Abstract
This chapter outlines how the development of a research-practice partnership (RPP) with the Office of Global and Multilingual Education of the Madison Metropolitan School District puts the Wisconsin Idea into practice. The genesis of this partnership was a local school principal and parent council’s desire to build a sense of belonging and community in a school that served an increasingly culturally, linguistically, and socially diverse school population. Collectively, we explored a central question of inquiry, “How can teaching and learning be reshaped to leverage diversity, particularly linguistic diversity, as a resource for all?” This chapter articulates how practice drove theory-building to foster linguistic and cultural collaboration within the microcosm of the classroom and school, and its impact in developing young students’ self-awareness and preparedness to engage as global citizens. We highlight examples of how through this RPP, the team supported and expanded opportunities for student and family engagement, as well as how learning from this partnership reaches across wider international contexts.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".