Teachers’ Awareness and Management of the Social, Cultural, and Political Indexicalities of Translanguaging
Bibliographic record
Abstract
Translanguaging scholars have debated whether dismantling boundaries between “named” languages is necessary for social justice in education. To explore this issue, we examined teachers’ reported use of named languages or translanguaging in classroom activities. We used a survey as an interview protocol to compare the extent to which four primary teachers in different international settings implemented two types of bi/multilingual practices with a recently taught class: translanguaging to learn without regard for boundaries between named languages, and symbolic valuation of students’ (named) home languages and languages of affiliation. Using the sociolinguistic construct of “indexicality” as a lens of analysis, we found that only sometimes do teachers describe attaching positive indexicalities (social, cultural, or political meanings) to dynamic translanguaging or to named languages, and only sometimes are these indexicalities egalitarian—suggesting that the answer to the debate lies in positionings teachers create while marshalling translanguaging or named languages to manage classroom identities.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".