Measuring electoral integrity: using practitioner knowledge to assess elections
Bibliographic record
Abstract
The integrity of the electoral process is vitally important for the delivery of democracy. However, there is an ongoing debate about how the integrity of elections can be measured. This article makes the theoretical and normative case for the use of practitioner knowledge. Unlike public and expert perceptions, electoral officials have unique practice-based, experiential, tacit knowledge about the conduct of elections, and more insights about the technical aspects of administration of which the public and even experts may be unaware. The article presents results from the first ever cross-national datasets based on a survey of electoral officials in 31 countries. Practitioner assessments are then compared to expert and public assessments, the traditional methods for assessing electoral integrity, and are found to be a reliable measure of electoral integrity. Analysis also shows that gender does shape practitioner assessments, suggesting that some electoral malpractices might be gendered in nature. Job satisfaction is also significant, which suggests that it should be controlled for in future studies. Overall, this study is significant for identifying the utility of a new method for assessing electoral integrity and provides important lessons for how they should be surveyed in the future.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".