Always a Bridesmaid: A Machine Learning Approach to Minor Party Identity in Multi-Party Systems
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In multiparty systems, maintaining a distinct and positive partisan identity may be more difficult for those who identify with minor parties, because such parties lack the rich history of success that could reinforce a positive social standing in the political realm. Yet, we know little about the unique nature of minor partisan identities because partisanship tends to be most prominent in single-member plurality systems that tend toward two dominant parties, such as the United States. Canada provides a fascinating case of a single-member plurality electoral system that has consistently led to a multiparty system, ideal for studying minor party identity. We use large datasets of public opinion data, collected in 2019 and 2021 in Canada, to test a Lasso regression, a machine learning technique, to identify the factors that are the most important to predict whether partisans of minor political parties will seek in-group distinctiveness , meaning that they seek a different and positive political identity from the major political parties they are in competition with, or take part in out-group favouritism , meaning that they seek to become closer major political parties. We find that party rating is the most important predictor. The more partisans of the minor party rate their own party favourably, the more they take part in distinctiveness. We also find that the more minor party partisans perceive the major party as favourable, the more favouritism they will show towards the major party.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it