Community Translation and Interpretation Services in Settlement Agencies: Perspectives from Translators and Interpreters in New Brunswick, Canada
Bibliographic record
Abstract
Smaller Canadian provinces have welcomed record numbers of newcomers in recent years, facing large-scale settlement support challenges. Language is often seen as a barrier as it slows down the integration process. One tool for facilitating communication and access to information is community translation and interpretation (CTI). To date, no data are available regarding CTI services procurement in the province of New Brunswick. In order to provide an overview of CTI services made available to newcomers in New Brunswick settlement agencies, I conducted mixed-method research com bining surveys sent to staff and to community translators and interpreters working in various agencies, as well as interviews with newcomers. This article examines the realities and perspectives expressed by the translators and interpreters. The research gives a clearer understanding of who they are and how they offer their services. As a matter of fact, community transla tors/interpreters are usually volunteers who have little to no training. While relying on non-professionals can cause different challenges, translators and interpreters express aneagernesstoreceivetraining.Theresearch,conducted in partnership with the New Brunswick Multicultural Council, is the first step in a broader research project that aims to depict the situation of CTI services in Atlantic Canada.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.060 | 0.017 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".