Party, Ideology, and Vote Intentions: Dynamics from the 2002 French Electoral Panel
Bibliographic record
Abstract
The debate over the relative importance of ideology versus party for vote choice in France is enduring. Resolution of the debate would have much value, for the light shed on sources of stability and change in multiparty electoral systems generally. The main reason the debate continues is that previous studies examining that question have been plagued by difficulties pertaining to variable measurement, model specification, election type, and research design. We address these problems and provide new evidence from the 2002 French Electoral Panel. Most notably, these data allow stronger causal inference because party identification and ideological identification are both measured in the first wave of the survey, that is, before the declaration of vote actually occurs. We estimate a multi-equation model of first-round legislative vote intention—as measured in the second wave of the panel—using two-stage least squares, ordered logit, as well as binomial and multinomial logit techniques. The results indicate that ideological identification systematically outweighs party identification in shaping the French voter’s choice.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".