Implementing and managing remote public service interpreting in response to COVID-19 and other challenges of globalization
Bibliographic record
Abstract
Although it has been acknowledged that public service interpreting helps reduce the language barriers faced by migrant populations, these barriers continue to be a significant cause of healthcare inequality. With the current COVID-19 pandemic, remote interpreting appears to be the most appropriate solution to address the health inequalities of migrant populations while intervening to reduce the risk of the virus spreading. The purpose of the research was to identify ways of providing a framework for the remote interpretation of public service encounters in the province of Quebec, Canada. A series of recommendations available in the literature were discussed with 27 key actors in the field during focus groups and individual conversations. A thematic analysis of participant discourse allowed us to confirm the extent to which existing recommendations were applicable, to clarify certain recommendations and to add seven new ones. TheGuide to the planning and practice of remote public service interpreting(see appendices) consists of 10 recommendations on the planning and management of remote interpreting services and 25 recommendations on the actual encounter. Results show that remote interpreting does not refer solely to telecommunications technology, but also to a knowledge and skill set needed to supervise and coordinate the use of that technology in very specific practice contexts while minimizing the effect of the virtual presence and encouraging the distribution of information among key actors through clearly identified communication channels. TheGuideaddressed these many features.
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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 | 0.041 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".