Democratizing the measurement of democratic quality: Public attitude data and the evaluation of African political regimes
Bibliographic record
Abstract
The emerging literature on the "quality of democracy" promises to advance our knowledge of democratization in several ways. First of all, it takes us beyond the narrow assessment of stability and endurance of democratic political regimes to ask about the quality of democracy those regimes supply. We move from asking "how stable?" to "how well?" Second, the concept of quality promises to provide us with greater nuance and precision, and thus greater ability to distinguish amongst widely disparate countries -- such as Cape Verde and Ghana on one hand, and Canada and Greece on the other -- that are usually lumped together as free, or as liberal democracies by the relatively blunt measures provided by Freedom House or Polity. Finally, and related to this, it enables us to move beyond "whole system" (Diamond 2002) measures and brings into focus differing dimensions of democracy, allowing us to appreciate that some countries can do better on some dimensions but worse on others. This also opens up the possibility that we may be able to measure democratic qualities in countries that do not qualify as electoral or liberal democracies (Elkins 2000).
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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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".