What explains public support for Canada's supply management regime?
Bibliographic record
Abstract
Abstract We investigate elasticity of policy preferences to information about the economic effects of policy tools. We survey approximately 5,000 people and ask a referendum question about liberalizing supply management in Canada. Supply management regulates production and marketing of dairy and poultry products in Canada through production restrictions, regulated pricing, and import barriers. Support varies widely across political‐party affiliation, and across individuals with different views on redistributive fiscal policies, international trade liberalization, and perceptions of how supply management affects food prices. We estimate causal effects of information about personal costs and distributional effects of supply management on support for the policy in a randomized experiment. Treated participants receive personalized information about how supply management affects household grocery costs, and information about the policy's distributional effects. Policy support is responsive to information treatments, but these effects are small relative to differences in support across individuals' views on economic issues such as international trade and fiscal redistribution policies. We find little evidence of heterogenous treatment effects across respondent characteristics, suggesting the effects of our information treatments are not tied to views about political and economic issues.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".