Playing with funds of difficult knowledge: interactional insights for heritage language education
Bibliographic record
Abstract
Abstract Migration stories are at the heart of how many immigrant-background Heritage Language Learners (HLLs) construct a sense of home, community, and identity across spatiotemporal scales. Nevertheless, narratives containing difficult knowledge (e.g., about war) are generally seen as threats to, rather than as assets in language learning and in education more broadly, and as such, are rarely drawn on in classrooms. In this paper I analyse excerpts from a group interview that I conducted with four grade-four girls during a year-long ethnographic case study. In particular, I examine how we all used various linguistic and paralingiustic resources to construct play frames. The play frames created a lower-stakes space in which to navigate the emotionally complex cultural memories that my interview questions about origins and migration prompted. The findings have implications for how language teachers listen to and engage with their HLLs’ funds of difficult knowledge.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".