The cognitive-conceptual, planning-organizational, affective-social and linguistic-discursive affordances of translanguaging
Bibliographic record
Abstract
Abstract The translanguaging turn in language education offers a new perspective on multilingualism by positing that multilingual learners have one linguistic repertoire rather than two or more autonomous language systems (García, O. & L. Wei. 2014. Translanguaging: Language, bilingualism and education. Palgrave MacMillan). When learners engage in translanguaging, they draw on all the features from their repertoire in a flexible and integrated way (Otheguy, R., O. García & W Reid. 2015. Clarifying translanguaging and deconstructing named languages: A perspective from linguistics. Applied Linguistics Review 6(3). 281–307. DOI:10.1515/applirev-2015-0014). While much of the current literature on language teaching advocates teachers’ use of pedagogical translanguaging, less research has focused on the pedagogical affordances of student-led translanguaging, especially in contexts with dominant monolingual norms. This paper presents the results of a case study exploring the affordances of translanguaging in two multilingual Grade 5 English language classrooms in Malaysia where English-only policies and practices were enforced by the teachers, but where translanguaging was used agentively by learners during their peer-to-peer interactions. The primary data sources for this six-month-long study included 100 30–90 min-long video recordings of 55 learners working together in small groups on various collaborative language learning activities, and member-checking interviews with the learners. The study was grounded in sociocultural theory and translanguaging, and employed a methodology of sociocultural discourse analysis. The results of the analysis revealed that in both classrooms, learners resisted the English-only policies and practices by using translanguaging widely and strategically throughout their collaborative peer-to-peer interactions. The use of translanguaging fulfilled 100 important cognitive-conceptual, planning-organizational, affective-social and linguistic-discursive functions that supported their individual and collective learning. The results of this study provide us with a view of translanguaging as collaborative and agentive, socioculturally situated and culturally responsive, and a resource for learning as well as a process of learning. The study makes recommendations for a language learning pedagogy that creates opportunities for learners to move language policies from the ground up through their collaborative use of translanguaging.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".