Perceptions of Electoral Integrity, (PEI-8.0)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This dataset by the Electoral Integrity Project evaluates the quality of elections held around the world. Based on a rolling survey collecting the views of election experts, this research provides independent and reliable evidence to compare whether countries meet international standards of electoral integrity. PEI-8.0 cumulative release covers 480 national parliamentary and presidential contests held worldwide in 169 countries from 1 July 2012 to 31 December 2021. For each contest, approximately 40 election experts receive an electronic invitation to fill the survey. The survey includes assessments from 4,591 election experts, with a mean response rate of 23%. The study collects 49 indicators to compare elections. These indicators are clustered to evaluate eleven stages in the electoral cycle as well as generating an overall summary Perception of Electoral Integrity (PEI) 100-point index and comparative ranking. The datasets are available for analysis at three levels: COUNTRY-level (169 observations); ELECTION-level (479 observations), and also EXPERT-level (4,590 observations). Each dataset can be downloaded in STATA, CSV, and EXCEL formats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.545 | 0.010 |
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 it