Re-voicing conceptualizations of plurilingual education: ‘El plurilingüismo, este concepto de … ¿cómo se puede decir?’
Bibliographic record
Abstract
Plurilingual education is usually viewed by its proponents as emancipatory. But is it really, and if so, how? For what kind of learning experiences, what kind of production of knowledge, what sense of identity and of community? In this contribution, we discuss collaborative inquiry and arts-based engagement as public and plurilingual pedagogy. We focus on a partnership between elementary school teachers, families, museums and university researchers established to investigate how multi-situated practices and ways of knowing can be leveraged as resources to inform our understandings of plurilingualism and plurilingual education. At the same time, we explore the role those actors can play to support engagement and reflexive inquiry through sensory and visualizing experiences in collaborative participatory research as powerful ways to cultivate reciprocity and relationality. The contribution aims to trigger a discussion about the importance of revoicing our conceptualizations of plurilingual education to include the discourses, multisensory experiences and stories of diversely situated social actors. Truthful, trustworthy pluri-dialogic and multilateral relations between partners open up a pathway to frame and claim alternative and transdisciplinary epistemologies of diversity, which can disrupt and displace the hegemonic Eurocentric matrix of Language Education.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.075 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".