Bibliographic record
Abstract
Contemporary realities of global population movement increasingly bring to the fore the challenge of quality and equitable health provision across language barriers. While this linguistic challenge is not unique to immigration contexts and is likewise shared by health systems responding to the needs of aboriginal peoples and other historical linguistic minorities, the expanding multilingual landscape of receiving societies renders this challenge even more critical, owing to limited or even non-existing familiarity of modern and often monolingual health systems with the particular needs of new linguistic minorities. The centrality of language to health beliefs, attitudes, practices, cultural scripts, and conceptual frameworks emphasizes its pivotal role in the healthcare process, and consequently in the adverse effects of treatment that is language-insensitive and unaware. Such an attitude on the part of medical authorities risks considerable epistemic injustice in the form of a (mis)judgement of patients' intelligence, credibility, and rationality based on the language that they speak and the manner in which they speak it, consequently impacting the quality and equity of care provided. This danger, I argue, may be effectively countered by fostering among the participants in the healthcare process a sense of epistemic humility through greater metalinguistic awareness. Outlining a range of operative steps that can be used to facilitate this. I argue that the reality of language barriers in the healthcare process, while not entirely eliminable, may nevertheless be successfully addressed, in order to mitigate the challenge of quality and equitable healthcare provision in multilingual societies.
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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 | 0.037 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.108 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.011 | 0.013 |
| 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".