Always a Bridesmaid: A Machine Learning Approach to Minor Party Identity in Multi-Party Systems
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 seekin-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 inout-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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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".