Replication data for: Are voters’ views about proportional outcomes shaped by partisan preferences? A survey experiment in the context of a real election
Bibliographic record
Abstract
We examine citizens’ evaluations of majoritarian and proportional electoral outcomes through an innovative experimental design. We ask respondents to react to six possible electoral outcomes during the 2019 Canadian federal election campaign. There are two treatments: the performance of the party and the proportionality of electoral outcomes. There are three performance conditions: the preferred party’s vote share corresponds to vote intentions as reported in the polls at the time of the survey (the reference), or it gets 6 percentage points more (fewer) votes. There are two electoral outcome conditions: disproportional and proportional. We find that proportional outcomes are slightly preferred and that these preferences are partly conditional on partisan considerations. In the end, however, people focus on the ultimate outcome, that is, who is likely to form the government. People are happy when their party has a plurality of seats and is therefore likely to form the government, and relatively unhappy otherwise. We end with a discussion of the merits and limits of our research design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".