Pedagogy of Whole Languaging Hearts: Fostering Relational Ways of (Re)Connecting to the Land With Multilingual Refugee Children
Bibliographic record
Abstract
ABSTRACT Colonial monolingual norms are a present oppressive force within schooling spaces, with a direct assimilative target on the linguistic practices of historically marginalized peoples, histories, and knowledge systems. For racially minoritized multilingual refugee learners, the space of in‐school science learning can be experienced as an involuntary detachment from linguistic wholeness, intergenerational ways of knowing, and community practices. Our work offers a disruption of the deficit‐based narratives that leave the brilliances associated with multilingualism invisibilized. Based on facilitator reflections and video‐based interaction analyses, our findings shed light on the pedagogy of whole languaging hearts that actively resist colonial and racialized linguistic norms and center social interactions and relations transcending divides across languages, geopolitical locations, and species. The pedagogy of whole languaging hearts was enacted to embrace how children and teachers located themselves within land‐based networks of interrelated communities. We offer to look at this network intersectionally, lovingly, and relationally to imagine otherwise, refusing the predefined disciplinary possibilities for eco‐ and socially just science futures together. Extending further the literature on translanguaging, our article focuses on the transformative possibilities of languaging emerging from our work with diasporic refugee children hailing from the “Middle East,” which transcends the borders of the named languages of Arabic, Kurmanji, and English. With the pedagogical enactment of languaging with whole hearts, we demonstrate how teachers can open space for science teaching grounded in the lives and worlds of multilingual refugee children and in embodied interactions on the land. We argue that ways of coming to know sciences are inseparable from our sense of community, stories, and relations.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".