Multilingual Crisis Communication Beyond Translation: Canadian Lessons from the Pandemic1
Bibliographic record
Abstract
This article presents a case study that examines multilingual communication practices in Canada’s Indigenous and non-official languages, as facilitated by Indigenous Services Canada (ISC) and the Public Health Agency of Canada (PHAC) during the COVID-19 pandemic. Despite a lack of reliable language-related data and the absence of a multilingual crisis communication policy framework, these two federal entities integrated a multilingual lens into their multipronged communication strategy, which included but was not limited to translating critical information. Their communication strategy resulted not only in information on a wide range of topics being available in an unprecedented number of languages/dialects, but also in the emergence of bottom-up multilingual communication practices, developed in collaboration with community-based organizations and Indigenous governments, that served to build trust and effect behavioural change. The article further argues that the emergence of these practices can help us to rethink language management in Canada in general and redefine the roles that translation and translators might play in multilingual digital communication, particularly in the age of multilingual artificial intelligence (AI), when language professionals are expected to renegotiate and broaden their professional mandates (Slator, 2024).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.045 | 0.017 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 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".