Controlling Roma refugees with ‘Google-Hungarian’: Indexing deviance, contempt, and belonging in Toronto's linguistic landscape
Bibliographic record
Abstract
Abstract This article investigates signage in the linguistic landscape of Toronto that is addressed to Hungarian-speaking Roma asylum applicants, focusing on multilingual public-order signs that convey warnings or prohibitions. Such signs are produced by institutional agents who often use machine translation (Google Translate), yielding ungrammatical texts in ostensible Hungarian. Drawing on ethnographic interviews, the article explores the indexicalities that such multilingual signs have for different groups of participants, including Roma addressees and English-speaking ‘overreaders’. While institutions may view the production of multilingual signs as indexical of open-mindedness towards migrants, Roma interviewees may see public-order signs as indexing racial stereotypes by presupposing deviant behavior, and may view ungrammaticality as indexing an unwillingness to engage in face-to-face interaction. (Multilingualism, Canada, Gypsies (Roma), linguistic landscapes, Hungarian, machine translation, indexicality)
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".