The Determinants of Electoral Registration Quality: A Cross-National Analysis
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
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it