From language portraits to language playlists: Exploring sonic possibilities for language autobiographical research
Bibliographic record
Abstract
Abstract Inspired by the rich body of research that has used language portraits as a means to invite reflection on people’s lived experiences of language in relation to the body, this study asked: What happens when we ask multilinguals to bring music to an interview about their lived experiences of language? In this paper, I analyze excerpts from two interviews with one couple, Mira and Andrej, to examine how the autobiographical narratives they produced about their language portraits or while sharing their language playlists differed in terms of content, amount of detail, and affective descriptions. I drew on (Goffman, E. (1974) Frame Analysis: An Essay on the Organization of Experience. Harvard University Press) concept of frame to examine the possibilities that sonic texts (songs) engendered within the Spracherleben (Busch, B. (2017) ‘Expanding the Notion of the Linguistic Repertoire: On the Concept of Spracherleben—the Lived Experience of Language’, Applied Linguistics, 38: 340–58) interviews. Overall, I found that language playlists had unique affordances in terms of the amount of narrative detail they seemed to prompt, affective engagement, and interpersonal connection. I conclude by presenting implications of this work, and invitations for future researchers in this area.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 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".