Linguistic and Cultural Collaboration in Schools: Reconciling Majority and Minoritized Language Users
Bibliographic record
Abstract
This article extends the work of culturally sustaining pedagogy by moving towards the conceptualization of linguistic and cultural collaboration (LCC) in classrooms through reconciliation of majoritarian and minoritized language users. Whereas attention in mainstream educational research has been given to students’ cultures, this article underscores that explicit attention to diverse languages and language varieties is essential to reconfiguration of power relations in schools and reconciliation among culturally and linguistically minoritized and dominant groups. Drawing on scholarship regarding plurilingual and multilingual practice, the authors conceptualize LCC as both a process and a product that expands all students’ critical multilingual language awareness. They draw on an ongoing research‐practice partnership (RPP) with a U.S. school district experiencing growing cultural and linguistic diversity. The article focuses on a single school to illustrate how LCC has been taken up as a whole‐school approach to leveraging students’ and families’ cultural and linguistic resources as vital to learning and living together in a multicultural and multilingual world. After outlining development of the RPP following a social design–based methodology, the authors discuss how in practice reconciliation is forged as teachers, students, and their families engage in collaborative multilingual bookmaking. They focus on three aspects of reconciliation (collaboration, restoration, living together) that support students in becoming more language‐aware and in moving towards multilingual activism.
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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.030 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.003 | 0.041 |
| Research integrity | 0.002 | 0.003 |
| 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".