Canadians’ trust in government in a time of crisis: Does it matter?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it