Does Tactical Voting Matter? The Political Impact of Tactical Voting in Canadian Elections
Bibliographic record
Abstract
Tactical voting primarily takes place under single-member district plurality electoral institutions and takes the form of third-party supporters voting for one of the major parties. Although much has been written about tactical voting, few studies have attempted to show its impact on seat distribution within the parliament or on the makeup of the subsequent government, in countries with single-member plurality systems. In this article, we assess the magnitude and impact of tactical voting in0 the Canadian general elections between 1988 and 2000. We build a model of tactical voting by identifying factors that are known to affect the level of tactical voting that we can measure using available data. Based on this model, we generate predicted levels of tactical voting for all parties within each district, and then use these predicted values to adjust the actual election data to produce a new set of data containing a would-be election outcome in the absence of tactical voting. By comparing actual election data, adjusted election data, and the seat share of political parties in the parliament after these elections, we discuss the political impact of tactical voting in Canada. The results of our study affirm that, in some cases, tactical voting does lead to election outcomes different from those in its absence and that arguments based on voter rationality are to some degree valid in the real world. At the same time, our results demonstrate that the impact of tactical voting on election outcomes, and thus on the actual distribution of seats within the parliament, has been minimal in Canada. It had no impact on the partisan composition of the government in any of the four elections studied.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".