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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".