Privilege of Voice as a Criterion for Sociolinguistic Inequalities
Bibliographic record
Abstract
Drawing on a theoretical framework for describing sociolinguistic inequalities with the notions of scope and access, the idea of voice can be used to define the aim of language learning processes as strategies to overcome sociolinguistic inequalities and marginalization. Understood in the metaphorical use of “being able to speak” and “to be heard” as used by Dell Hymes, having a voice must be analyzed from an intersectional perspective as a privilege. Studying the example of a mobile speaker and her successive attempts to find voice in different linguistic relations, reveals that the listener and their attitude have to be included in discussions of the conditions for having voice. For the rigor of sociolinguistic arguments, voice should be reserved for use on an abstract level while drawing on other (sociolinguistic) notions in empirical analysis that can describe concrete manifestations of linguistic inequalities.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.049 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".