The Extraterritorial Voting Rights and Restrictions Dataset (1950–2020)
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 paper introduces the Extraterritorial Rights and Restrictions dataset (EVRR), the first global time-series dataset of non-resident citizen voting policies and procedures. Although there have been previous efforts to document external voting, no existing data source simultaneously captures the scale (195 countries), time frame (71 years), and level of detail concerning extraterritorial voting rights and restrictions (over 20 variables). After a brief overview of prior datasets, we introduce EVRR coding criteria with a focus on conceptual clarity and transparency. Descriptive analysis of the dataset reveals both the steady expansion of extraterritorial voting as well as several regional and temporal trends of voting rights restrictions. Finally, we revisit and extend the work of two groundbreaking cross-national studies focused on the causes and effects of external voting rights. Using EVRR data we demonstrate that including more fine-grained aspects of extraterritorial voting provisions in these analyses improves understanding of important political and economic outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| 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