Party Leadership framing of Election results
Bibliographic record
Abstract
The survival of party leadership is dependent on how partisans (and others interpret election outcomes. Do partisans consider the election outcome as a victory or a defeat? Previous research provides evidence of bias in the evaluation of election results (i.e., partisans are less likely to acknowledge electoral defeats of their own political party). In this project, I investigate if party leadership framing of outcomes may shape evaluations of election results among party supporters and the electorate at large. To this end, I conduct survey experiments in the aftermath of the Canadian federal election and the Danish local election, both held in 2021. In the survey experiments, I ask citizens whether they perceive individual parties as winners or losers of the election. Respondents are either randomly assigned to a baseline condition receiving no information at all, a control condition in which the electoral outcome is described in neutral terms or one of two conditions in which one of the party leaderships (in Canada either the Liberal Party or the Conservative Party; in Denmark either the Social Democrats or the Liberal Party) provide an additional statement about why the election has been a success for their particular party.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 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".