A Dialogue on Public Health Celebrities during COVID-19
Bibliographic record
Abstract
This dialogue offers a transnational perspective on the emergence of public health officials (PHOs) as celebrities during the acute phase of the COVID-19 pandemic. Drawing on scholarship on public health rhetorics (e.g., Keränen 2014; Malkowski & Melonçon 2019) and on our experiences of living through and observing the ongoing pandemic in Australia, Canada, China, and the United States, we focused our discussion on our local contexts; key public health celebrities who emerged in those contexts; changes in public reaction to those figures over time; and why the celebrification of public health figures is of interest to scholars in rhetoric of health and medicine. We close by reflecting on how our transnational discussion of public health celebrities has reshaped our understanding of celebrification in health and outline key areas of future collaboration and inquiry.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.049 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.010 | 0.019 |
| 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".