The impact of discrimination on trust in government institutions: A LASSO regression analysis in the Canadian context during COVID-19
Bibliographic record
Abstract
This study examines the effect of discrimination on public trust in government and public servants during the COVID-19 pandemic. Using Canadian survey data collected in 2020 (N = 36,674), we apply both logistic regression and ordinary least squares (OLS) regression to analyze how discrimination related to COVID-19 influenced trust in four public institutions. Prior to running these models, we used the Least Absolute Shrinkage and Selection Operator (LASSO) method for variable selection. The findings indicate that personal experiences of discrimination significantly reduce institutional trust, particularly when discrimination occurs online, in the workplace, or during interactions with the police. However, the results also show that a strong sense of belonging—whether to Canada, a specific province or territory, or a shared community (such as speakers of the same language)—is associated with higher levels of trust in institutions. These insights provide valuable guidance for policymakers and public officials.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".