Behavioural insights policies in Canada: support for nudges vs nudging
Bibliographic record
Abstract
Abstract Over the past twenty years, behavioural insights and nudges have gained prominence in public policy design. Public opinion research on this subject has largely considered two questions: (1) who supports nudges? and (2) where is support for nudges strongest? Using data from two nationally representative surveys fielded in 2023 and 2024 (N = 2020 and N = 1991), we take up these questions in Canada—a ‘principled pro-nudge’ country. We measure opinion toward 30 nudge policies across three policy domains—15 that provide a benchmark to other country studies, coupled with 15 that reflect policies that were implemented by Canadian nudge units. We also analyze open-ended responses to a question that asks what individuals think of nudging (if they think of them at all). We find that approval for nudges is high, with 71% of respondents supporting nudges that have been implemented in Canada. Second, we identify similar gender, ideological and identity-based patterns for support as observed in countries with different social and market structures. Third, analyzing open-ended responses that gauge respondents’ thoughts on BI, our findings highlight the complicated nature of public opinion toward BI, which includes optimism alongside uncertainty and skepticism.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".