Perceptions of Electoral Integrity-UK, (PEI-UK 2.0)
Bibliographic record
Abstract
This codebook describes an expert survey on Perceptions of Electoral Integrity (PEI). The original PEI survey asks experts to evaluate electoral integrity after national-level elections around the world. However, the Project has also occasionally provided data on sub-national elections. This codebook describes all variables included in the dataset release of October 2024 of the most recent subnational elections in the UK (release PEI_UK_2.0). This study is conducted by Toby S. James, Holly Ann Garnett, and Sofia Caal-Lam for the Electoral Integrity Project based at the Royal Military College of Canada, Queen’s University, and the University of East Anglia. This survey was originally designed and conducted by Pippa Norris and the Electoral Integrity Project team at the University of Sydney and Harvard University (2012-2018). The dataset is drawn from a survey of 120 expert assessments of electoral integrity across sub-national units in the United Kingdom. The elections were the elections to the Scottish Parliament on 6 May 2021, the Senedd Cymru/Welsh Parliament on 6 May 2021 and the Northern Ireland Assembly on 5 May 2022. To operationalize the concept of electoral integrity, the PEI asks experts to evaluate elections using 62 indicators, grouped into eleven categories reflecting the whole electoral cycle. Two files are released in PEI_UK 2.0: an EXPERT-level file (38 observations) and an ELECTION-level file (3 observations). Each dataset can be downloaded in the EXCEL formats.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.093 | 0.026 |
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".