PEI US 2018 state-level (PEI_US_2018) 14-03-2020.tab
Bibliographic record
Abstract
This data-set by the Electoral Integrity Project evaluates the integrity of the US congressional election held on 6 November 2018. Based on a survey collecting the views of US-based political scientists, this research provides independent and reliable evidence to assess whether the 2018 congresional election met international standards of electoral integrity. The survey asks respondents to evaluate how the election was conducted in each of the 50 US states plus the District of Columbia. The study collects 49 indicators to measure electoral integrity. 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 of US states. The datasets are available for analysis at two levels: STATE-level (51 cases); and EXPERT-level (574 cases). Each dataset can be downloaded in STATA, SPSS, CSV and R formats. PEI_US_2018 compiles the responses of 574 experts, representing an overall response rate of 14%. The study is conducted by Pippa Norris, Max Grömping, and Holly Ann Garnett for the Electoral Integrity Project based at the Universities of Sydney and Harvard.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.201 | 0.246 |
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".