Referendum Voting as Political Choice: The Case of Quebec
Bibliographic record
Abstract
In an article published in this Journal , Nadeau, Martin and Blais argue that perceptions of the costs and benefits of alternative outcomes and general orientations to risk interact to affect voters' decisions in referendums on fundamental political questions such as Quebec sovereignty. We use Nadeau et al .'s data to demonstrate that their interaction-effects model is overly complex and suffers from serious multicollinearity difficulties. A simpler main-effects model has virtually identical explanatory power and removes anomalous findings. We also argue that their model is too simple because it omits variables such as party identification, feelings about party leaders and government performance evaluations that voters use as heuristic devices to help them make decisions when stakes are high and information about the costs and benefits of referendum outcomes is low. We analyse a dataset that includes these variables and demonstrate that they have strong effects in a model of referendum voting that controls for perceived costs and benefits of alternative referendum outcomes and several other variables. Additionally, differences in the magnitudes of the perceived costs and perceived benefits of alternative referendum outcomes are not statistically significant. This latter finding contradicts widely cited experimental results in behavioural economics and related ‘asymmetry’ hypotheses concerning the presumed status quo bias in major referendums.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".