Ideological Clarity in Multiparty Competition: A New Measure and Test Using Election Manifestos
Bibliographic record
Abstract
Parties in advanced democracies take ideological positions as part of electoral competition, but some parties communicate their position more clearly than others. Existing research on democratic party competition has paid much attention to assessing partisan position taking in electoral manifestos, but it has largely overlooked how manifestos reflect the clarity of these positions. This article presents a scaling procedure that better reflects the data-generating process of party manifestos. This new estimator allows us to recover not only positional estimates, but also estimates for the ideological clarity or ambiguity of parties. The study validates its results using Monte Carlo tests, a manifesto-drafting simulation and a human coding exercise. Finally, the article applies the estimator to party manifestos in four multiparty democracies and demonstrates that ambiguity can enhance the appeal of parties with platforms that become more moderate, and lessen the appeal of parties with platforms that become more extreme.
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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 | 0.028 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".