It’s Just a Little High: Testing the Effect of the Legalization of Marijuana on Voters’ Behaviour in Canada
Bibliographic record
Abstract
A fundamental question about voting behaviour is whether voters lead parties and politicians by judging them on the ground of their issue stands, or whether they instead align their views to match those of the party and politicians they favour. The recent legalization of marijuana in Canada offers an opportunity to untangle this causal knot. This paper makes use of panel data collected during the 2011 and 2015 Canadian elections to test whether voters direct their support towards parties and politicians in line with their prior issue preferences or whether, on the opposite, voters bend their issue preferences to match their partisan affiliation. Results provide evidence for the independent effect of the legalization of marijuana on voters’ behaviour towards the Liberal Party. More generally, this makes for a striking case of the effect of an easy, positional issue and contributes to the longstanding debate on issue voting in Canadian politics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".