Reimagining Accents and Speech Recognition with Sociolinguistic Perception Studies and Research on Listening Subjects
Bibliographic record
Abstract
This commentary unpacks how insights from studies of sociolinguistic perception and research on listening subjects may offer compelling ways to recognise the humanity and multiplicity of each voice, disclosing that perception is something listeners do.Below, I think with selected projects from the two strands of research to advance discussions on accent bias in technology, showing how listening is formed through practices operating within particular cultures of reception, where 'even the most silent of listeners is an author of an emergent narrative' (Ochs and Capps 1996: 21).I argue that the two strands enable us to reimagine all accents as loci 'of the experience and knowledge production of the modern' (Inoue 2003: 158), operating through particular practices of citation transcending 'observable and [. . .] recordable "realities"' (Inoue 2003: 182).This in turn enables more voices 'to be justly recognized' (Eidsheim 2023: 143), moving beyond only listening from positions of power to a better understanding of how affordances and infrastructures amplify, mishear or silence particular human soundings.This commentary is part of a Cross-Journal Symposium on Listening Practices and Linguistic Perception in which early career scholars engage with Inoue (2003) and six additional articles published in the
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.063 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".