Postmemory and multilingual identities in English language teaching: a duoethnography
Bibliographic record
Abstract
Postmemory, the narrativised intergenerational transfer of often traumatic experiences, is a crucial component of multilingual identity negotiation. In this article, we focus specifically on the curricular interactions and personal and collective aspirations of multilingual students who use English for academic purposes. We situate our discussions in the literature on critical pedagogy, affect/emotion theory, and memory studies. We utilise duoethnography as a methodology for our dialogic inquiry. A duoethnographic approach enables us to be both self-reflexive and socially transformative through our explorations of lived experiences of language loss and gain and of our historical becoming of professional language educators. We highlight how multilingual identities are constructed, challenged, and reconstructed not only by social practices of sign-use, but also by intergenerational spatial mobility and the distributed nature of postmemory. Finally, we provide pedagogical implications for language education that seek to foster critical affective literacies. Turning to affect and emotion is important to move the discussion of multilingual identities beyond physical signifiers of social differentiation (i.e. race, gender, ethnicity, and class). Pedagogical attention to memory, affect and identity may offer us a more nuanced understanding of teachers’ and students’ agency and investment in multilingual semiotic practices.
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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 | 0.008 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".