Bibliographic record
Abstract
Abstract In a world of increasing globalisation, governments, including Canada, face ongoing challenges in their efforts to integrate immigrant languages and to communicate with their users in public service settings. By exploring the translation policy in health care settings in Ontario, Canada, this research investigates how immigrant language barriers in health care access are addressed there, and probes into ideologies around the issue of immigrant language integration. Ontarian translation policy in health care settings is pragmatic yet cautious and laissez-faire. It indicates inclusiveness to accommodate immigrants; but it also reveals considerable tensions and hesitations. The belief that translation is a necessary measure to secure immigrants’ equal health care rights has been largely overridden at the regional and institutional level in Ontario, hindering further planning and more effective provision. The inadequate value designated to translation in terms of immigrant integration by government authorities, the ambiguous and ambivalent stances of Toronto Central Local Health Integration Network and some hospitals on translation provision against budgetary concern and the expectation for linguistic homogeneity all play roles in determining the flexibility and fluctuation of translation policy in health care settings in Ontario.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.030 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".