The Determinants of Electoral Registration Quality: A Cross-National Analysis
Bibliographic record
Abstract
Electoral registers provide the definitive record of who can participate in an election, but there is often thought to be considerable variations in their quality cross-nationally. This leads to concerns about eligible voters being de facto disenfranchised on election day; but also ineligible voters or fictitious names appearing on the roll which can enable electoral fraud. In either case, the legitimacy of the election can be questioned. The electoral register is also used for other purposes such as drawing electoral boundaries. This article introduces some common international terminology for electoral register quality and a conceptualisation of the different ways in which an electoral register can be compiled. It then introduces a new global dataset on registration procedures (n = 159). The article hypotheses that automatic voter registration, as well as organisational and structural factors, strongly affects accuracy and completeness. The results show that automatic voter registration increases the completeness of the electoral register and also has a positive impact on accuracy. The organisational performance of the electoral management body was also shown to have positive effects on completeness and accuracy, suggesting an additional means of improving electoral registers beyond the registration model, which also rest in the hands of policy makers.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".