Strategic and Sincere Voting in a One‐Sided Election: The Canadian Federal Election of 1997<sup>*</sup>
Bibliographic record
Abstract
Objective. We are interested in whether and how voters make strategic decisions in a race that is, according to the polls, expected to be very one sided. Looking specifically at the choices available to ideologically right‐of‐center voters in the 1997 Canadian federal election, we argue that strategic considerations will be filtered by voters' assessments of the competitiveness of the race both locally and nationally. Methods. We estimate logistic regression models measuring support for the two right‐of‐center Canadian political parties. Our models focus on the relationship between assessments of district‐ and national‐level party prospects on voting for the Progressive Conservative Party. Results. We find that voters who consider the race competitive emphasize district‐level data in their strategic calculations. However, those who consider the election to be all but over look more closely at national‐level concerns when deciding which right‐wing party to support. Conclusions. We conclude that earlier understandings of tactical voting should be updated to take into consideration the circumstances under which voters will use national‐level evaluations of relative party viability in casting their ballots.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 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) | 0.006 | 0.001 |
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".