The Dynamics of Party Identification Reconsidered
Bibliographic record
Abstract
This paper uses mixed Markov latent class models and data from multiwave national panel surveys to investigate the stability of individual-level party identification in three Anglo-American democracies—the United States, Britain, and Canada. Analyses reveal that partisan attachments exhibit substantial dynamism at the latent variable level in the American, British, and Canadian electorates. Large-scale partisan dynamics are not a recent development; rather, they are present in all of the national panel surveys conducted since the 1950s. In all three countries, a generalized “mover–stayer” model outperforms rival models including a partisan stability model and a “black–white” nonattitudes model that specifies random partisan dynamics. The superiority of generalized mover–stayer models of individual-level party identification comports well with American and British studies that document nonstationary, long memory in macropartisanship. The theoretical perspective provided by party identification updating models is consistent with the mix of durable and flexible partisans found in the United States and elsewhere.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".