Measuring and assessing <i>subnational electoral</i> democracy: a new dataset for the Americas and India
Bibliographic record
Abstract
Assessing how democracy varies within countries is paramount to the subnational turn in comparative politics. Despite recent contributions, we still lack a comparable measure of democracy for provinces inside countries. To overcome this limitation, I present the Index of Subnational Electoral Democracy (ISED), a measure that tracks the electoral dimension of democracy across the provinces of nine Latin American countries, the United States, Canada, and India for a period of roughly 40 years, making it the largest dataset on subnational regime outcomes to date. I then use the ISED to assess the democratic trajectories of Argentinian, Brazilian, Mexican, and Indian states, revealing that: 1) Indian provinces have been, on average, more democratic than their Latin American counterparts. 2) The relative position of provincial regimes within these countries has been remarkably stable over time. 3) Most subnational units in the Americas have had “low intensity” regimes. 4) Subnational regime hybridity has been the norm rather than the exception, and that 5) for the Latin American cases under consideration, democracy and development are positively connected at the local level. I conclude by outlining the ISED's research applications and reflecting on the implications of these five conclusions for future research on subnational democracy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".