The Nature of Party Categories in Two‐Party and Multiparty Systems
Bibliographic record
Abstract
Categories are one of the primary ways by which people make sense of complex environments. For political environments, parties are especially useful categories. By simplifying political life, party categories enable people to make sense of politics. A fundamental characteristic of party categories is that they minimize perceived differences of members within a party (e.g., two Democrats) and maximize perceived differences between members of different parties (e.g., a Republican and a Democrat). In two‐party systems, politicians in leftist parties will often be perceived as highly differentiated from politicians in right‐wing parties. Yet, in multiparty systems there is greater complexity and potential for confusion since there are often multiple parties on the left and/or right. Spatial models of political competition predict that ideologically close neighboring parties will be perceived as similar, yet a categorical perspective holds that the public will perceive parties on the same side of the ideological divide to be dissimilar. In the present article, we review a research program investigating how political parties are treated as categories and present new data from seven democracies showing that people perceive parties to be highly differentiated regardless of where parties are located in ideological space.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".