Don’t Blame the Messenger? An assessment of public regulations announcements and support for public policy and the governing party
Bibliographic record
Abstract
Since the start of the COVID-19 pandemic, Canadian public health officials, especially Chief Medical Officers of Health, have become increasingly more visible to the public, sometimes appearing alone or alongside elected officials to announce COVID-19 related public regulations. To what extent does varying the person chosen to announce such policies affect public support for both the policy and the governing party? To answer these questions, we will analyze data from a survey experiment to be fielded in Ontario in March 2022. Our experiment will manipulate who delivers the message. We expect that the public will be most supportive of policies pertaining to COVID-19 related regulations (annual booster shots) when they are: 1 – not announced; 2 - announced by Ontario’s Chief Medical Officer of Health, Dr. Kieran Moore; 3 - announced by Premier Doug Ford accompanied by Dr. Kieran Moore, and other members of his cabinet and; 4 - announced by Premier Doug Ford. We expect a similar pattern for political support. Such findings would suggest that, when these types of announcements are necessary, government manipulation of who delivers the message may be used for political gains, shifting the blame from elected officials to public servants.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".