Subnational Democracy in (Cross-National) Comparative Perspective: Objective Measures with Application to Argentina, Brazil, Canada, Mexico, Uruguay and the United States
Bibliographic record
Abstract
Efforts to operationalize democracy at the national level have occupied a central place in the discipline since the 1960s, and have resulted in a cumulative body of literature and in a variety of datasets of increasing rigor and geographic and temporal coverage. Attempts to measure democracy at the subnational level are much more recent and inchoate, cover only a few countries and periods, and, critically, are not comparable across nations. After reviewing the state of the subject at the national level and the existing (objective) national subnational indices, this paper proposes six versions of an objective Subnational Democracy Index that can be calculated on the basis of (typically available) electoral and institutional data. Because of their modest data demands, the proposed indices can easily be applied to very different national and temporal contexts, thus permitting comparisons of subnational regimes across countries. The measures are pilot-tested on the first-level subnational units of five federations (Argentina, Brazil, Canada, Mexico, and the United States) and of one unitary country (Uruguay). The ultimate goal of this line of research is to produce cross-sectional — time-series datasets of subnational democracy with broad geographic and temporal coverage similar to those existing for national regimes.
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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 | 0.003 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".