Who are “we”? Examining relational ethos in British Columbia, Canada's COVID-19 public health communication
Bibliographic record
Abstract
This paper investigates the multiple meanings and functions of the pronoun “we” in COVID-19 public updates by British Columbia's acclaimed Provincial Health Officer Dr. Bonnie Henry in 2020. Our rhetorical case study shows how “we” contributes to Henry's relational ethos by attempting to foster a communal identity with her implied audience while also distinguishing public health expertise, actions, and authority from citizens' knowledge and actions. Ambiguous uses of “we” blur the line between the knowledge and responsibilities of “we” in public health and “we” as citizens. Overall, our rhetorical analysis demonstrates the significant but ambivalent role this pronoun can play in building relations of social trust among citizens, experts, and institutions within public health and science communication contexts and it suggests the importance of judicious pronoun usage when communicators strive to foster these relations.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.044 | 0.034 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.009 |
| 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".