Language Change in Canadian Covid-19 Crisis Communication
Bibliographic record
Abstract
This paper focuses on how political communicators change their language when attempting to reassure their audiences. By their function, politicians necessarily threaten their audiences’ ‘negative face’ - the need to have one’s rights and liberties not restricted/removed. To appeal to their audiences, politicians may adopt at least two strategies. Firstly, they can appeal to their audience’s ‘positive face’, showing that they seek the audience’s approval. Secondly, they may threaten their audience’s ‘negative face’ indirectly rather than directly, with ‘redressive language’ to compensate for potentially ‘face-threatening behaviour’ (e.g. Grundy 2000). Through the framework of Brown & Levinson’s (1987) Politeness Theory, we will analyse the speech of Canadian Prime Minister Justin Trudeau to determine the extent to which he uses the two strategies above more in crisis contexts (COVID-19 press briefings) compared to non-crisis contexts. We expect that the two strategies outlined above will be used more frequently in crisis contexts. Based on our initial analyses, we also suggest that this is achieved via explicit appeals to listeners’ sense of collective belonging and social responsibility as Canadian citizens. References: Brown, Penelope, and Stephen C. Levinson. 1987. Politeness. Some Universals in Language Use. Cambridge: CUP. Grundy, Peter. 2000. Doing Pragmatics. 3rd ed. London: Hodder Education.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".