Harmonization in the Comparative Study of ElectoralSystems (CSES) Projects
Bibliographic record
Abstract
This chapter focuses on harmonizing election survey and macro data with insights from the Comparative Study of Electoral Systems (CSES) project, a cross-national study incorporating election studies in over 50 states over the past 20 years. Drawing on insights from all CSES data products, the chapter begins by introducing the CSES, the study design, and its data products. Next, we outline five core principles CSES adheres to in its harmonization efforts and describe the technical infrastructure underpinning our efforts. Section 6.3 outlines ex-ante input harmonization, including the CSES Module questionnaire and the macro data development. Section 6.4 focuses on ex-ante output harmonization, specifically how CSES approaches the harmonization of respondent demographic and party level data. Section 6.5 explores the interplay between ex-ante and ex-post harmonization that CSES encountered with creating the CSES Integrated Module Dataset (IMD). Finally, we summarize the chapter and briefly discuss new data harmonization frontiers, which CSES is embracing.
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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 | 0.146 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".