Comparative Study of Electoral Systems, 2001-2006
Bibliographic record
Abstract
This study is the full release of 2001-2006 data from Module 2 of the Comparative Study of Electoral Systems. The Comparative Study of Electoral Systems is an ongoing collaborative program of crossnational research among national election studies designed to advance the understanding of electoral behavior across polities. The project, which is being carried out in over 50 consolidated and emerging democracies, was coordinated by social scientists from around the world who cooperated to specify the research agenda, the study design, and the micro- and macro-level data that native teams of researchers collected within each polity. This collection currently comprises data from surveys conducted in the countries of Albania, Australia, Belgium, Brazil, Bulgaria, Canada, Chile, Czech Republic, Denmark, Finland, France, Germany, Great Britain, Hong Kong, Hungary, Iceland, Ireland, Israel, Italy, Japan, Kyrgyzstan, Mexico, Netherlands, New Zealand, Norway, Peru, Philippines, Poland, Portugal, Romania, Russia, Slovenia, South Korea, Spain, Sweden, Switzerland, Taiwan, and the United States. Module 2 focuses on electoral institutions and political behavior, particularly on the fundamental principles of democratic governance: representation and accountability. It aims to examine how well different electoral institutions function as mechanisms by which citizens' views are represented in the policymaking process, and by which citizens hold their elected representatives accountable. This is accomplished by explicitly linking individual attitudes and behaviors to the political context across a variety of settings. The module added a new set of items on citizen engagement and cognition across demographic polities, and expanded the analyses of the first module to examine how voters' choices are affected by the institutional context within which those choices are made. The survey results have been compiled and supplemented with district-level information that provides insight into the respondent's political context, and macro-level data that detail the respondent's political system as a whole. At each level of data collection, the measurements used have been standardized to promote comparison. Demographic variables include age, sex, race, ethnicity, education level, marital status, employment status, occupation, household union membership, language, socioeconomic status, political party affiliation, political orientation, religious preference, frequency of religious attendance, household income, number of children and other members of the household, and type of residential area (e.g., urban or rural).
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".