"People have probably offered to buy me a dictionary 20 times since I've been here": Risk management within a community of journalists in francophone Canada
Bibliographic record
Abstract
Gathering and writing news in a bilingual context increases the complexity of a practice already characterised by multitasking. Does this situation create particular risks? How do journalists deal with hazards? This article discusses the strategies of risk management that reporters develop as a community of practice and investigates what these strategies reveal about reporters' conception of language. To discover these strategies, I carried out fieldwork in a newsroom situated in Canada's National Capital Region: Ici Radio-Canada Ottawa–Gatineau, which is the francophone public service broadcaster that publishes multimodal content in French on various platforms (radio, television, a website and social media). I conducted semi-structured interviews, sessions of non-participant observation and gathered documents in the field. Participants are especially concerned by the risk of linguistic interference (Anglicisms) because they align with Ici-Radio Canada's model of linguistic prestige and, therefore, fear complaints from their audience. They mainly share these risks with their direct French-speaking colleagues on an ongoing basis and with the speech community of educated French speakers in a context where French is seen as a minority language and English is seen as a threat.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.019 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".