Bibliographic record
Abstract
This codebook describes a global expert survey of electoral management bodies undertaken during the summer of 2024. The Electoral Management Survey (EMS) 3.0 asks electoral officials about how elections are run in their respective jurisdictions. This codebook describes all variables included in the dataset release of December 2025. This study is conducted by Toby S. James, Holly Ann Garnett, Sonali Campion and Sofia Caal-Lam for the Electoral Integrity Project based at the University of East Anglia, the Royal Military College of Canada, and Queen’s University. This survey follows on from a survey undertaken in 2017-18 and 2023. This dataset is described as version 3.0. The survey was undertaken in collaboration with International IDEA who co-designed and supported the distribution of the survey. Overall, 244 EMBs were contacted across 241 countries. They were invited to complete the survey either online or via a Word doc. The appendix provides the survey circulated. It was provided in Arabic, German, English, Spanish, French, Portuguese and Russian. The survey contains responses from the 49 EMBs who responded. In most cases these were national EMBs, but some were regional EMBs.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.091 | 0.067 |
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".