Canadians’ trust in government in a time of crisis: Does it matter?
Bibliographic record
Abstract
The ability of governments and nations to handle crises and protect the lives of citizens is heavily dependent on the public's trust in their governments and related social institutions. The aim of the present research was to understand public trust in government during a time of crisis, drawing on interview data (N = 56) collected during the COVID-19 pandemic (2021). In addition to the general public (n = 11), participants were sampled to obtain diversity as it relates to identifying as First Nations, Métis, and Inuit (n = 7), LGBT2SQ+ (n = 5), low-income (n = 8), Black Canadians (n = 7), young adult (n = 8), and newcomers to Canada (n = 10). Data were coded in consideration of social theories of trust, and specifically the nature of trust between individuals and institutions working with government in pandemic management. Canadians' trust in government was shaped by perceptions of pandemic communication, as well as decision-making and implementation of countermeasures. Data suggest that although participants did not trust government, they were accepting of measures and messages as presented through government channels, pointing to the importance of (re)building trust in government. Perhaps more importantly however, data indicate that resources should be invested in monitoring and evaluating public perception of individuals and institutions generating the evidence-base used to guide government communication and decision-making to ensure trust is maintained. Theoretically, our work adds to our understanding of the nature of trust as it relates to the association between interpersonal and institutional trust, and also the nature of trust across institutions.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".