How many political parties are there, really? A new measure of the ideologically cognizable number of parties/party groupings
Bibliographic record
Abstract
We offer a new measure of the ideologically cognizable number of political parties/party groupings that is intended to be complementary to the standard approach to counting the effective number of political parties – the Laakso–Taagepera index (1979). This approach allows the possibility of precise measurement of concepts such as polarized pluralism or fragmented bipolarism and is applicable to both unidimensional and multidimensional representations of party locations. Using recent CSES (Comparative Study of Electoral Systems) data on one-dimensional representations of party locations in four real-world examples (two of which are available in an online appendix), we find that Slovenia, treated initially as a five-party system, has its optimal reduction as a two-bloc/party system, as does Spain, which is treated initially as a four-party system. However, Canada, treated initially as a four-party system, has its optimal reduction as a three-bloc/party system if we look at a unidimensional representation of the party space, while it remains a four-bloc system if we draw on Johnston’s two-dimensional characterization of Canadian political competition. Finally, the Czech Republic, initially a five-party system, is optimally reduced to a system with four party groupings.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".