Bibliographic record
Abstract
This paper contributes to research that has identified an institutional bias towards monolingualism in the legal sphere (Eades 2003, Haviland 2003), investigating how this ‘monolingual worldview’ (Ellis 2006) affects interactions between speakers of minority languages and courtroom professionals in New York City Small Claims Court. Drawing on ethnographic fieldwork and a data-set of 40 recorded arbitration hearings that include speakers of Spanish, Russian, Haitian Creole, and Polish, the paper shows that, while the courtroom itself is multilingual, all individuals besides the interpreters are expected to use one language only, even though most are multilingual to some degree. This ideology is evident in the common practice among legal professionals and interpreters to interrupt and discourage any use of English (i.e. codeswitching) by individuals who are accompanied by an interpreter. On the other hand, court users who avoid the use of English are frequently accused of deceit (‘pretending not to speak English’) by the opposing party. The court’s monolingual bias thus forces bilingual participants to act as monolinguals, thereby creating the appearance of monolingualism as the norm. It is argued that these practices inherently disadvantage minority speakers by preventing them from using the full range of their communicative abilities, and by making language choice a factor in the assessment of their credibility.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".