Bibliographic record
Abstract
This special issue of Ethnicities focuses on the phenomenon of linguistic racism. Linguistic racism constitutes the intersection of language, race/ism, and in/equality, as seen in racialized discourses on the relative status of languages and bi/multilingual language use, particularly as these are directed toward non-dominant language speakers. The theoretical framings underpinning the contributions in this issue draw on sociological discussions of critical race theory, and sociolinguistic and linguistic anthropological discussions of language ideologies, linguistic racism, and raciolinguistics. Racialized discourses of language (use) are situated within sociohistorical and sociopolitical contexts, grounded in nationalism and colonialism, that privilege dominant national and international languages, public monolingualism, and native-speaker competence in those languages. In contrast, related linguistic hierarchies of prestige pathologize the language uses of non-dominant language – often Indigenous and/or bi/multilingual – speakers and construct their language use in both overtly and covertly racialized terms. The result is regular linguistic discrimination and subordination experienced by non-dominant language speakers, inevitably framed within wider racialized institutional and everyday discursive practices. The contributions herein explore these issues in relation to Indigenous and other non-dominant language use(s), and their (mis)representation, in the media, workplace, and academia, in the contexts of New Zealand, Australia, Bangladesh, Canada, and the United States.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".