No one is safe until everyone is safe: Direction régionale de santé publique de Montréal’s risk-based approach to multilingual crisis communication
Bibliographic record
Abstract
Canada’s multiculturalism is situated within a bilingual framework that often restricts Canada’s linguistic diversity, which goes beyond its official languages. The limitations of this framework were exposed by the COVID-19 pandemic, during which government-led crisis communication strategies were guided by the country’s multilingual reality and the risks associated with ignoring it. This article focuses on a case study that examines multilingual communication strategies and practices coordinated during the pandemic by the Direction régionale de santé publique de Montréal. Drawing on documentary evidence and semi-structured interviews, the article reveals that Santé publique Montréal integrated a multilingual approach into its emergency communication strategy after the first wave of the pandemic, which resulted in more translations of COVID-19 information, and the implementation of bottom-up communication practices in collaboration with community-based organisations to build trust. The article also shows that the pandemic paved the way for a risk-based approach to language management capable of helping us rethink multilingualism management in Canada and beyond.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".