Bibliographic record
Abstract
In two decades of scholarship on hybrid regimes two significant advancements have been made. First, scholars have emphasized that the hybrid regimes that emerged in the post-Cold War era should not be treated as diminished sub-types of democracy, and second, regime type is a multi-dimensional concept. This review essay further contends that losing the lexicon of hybridity and focusing on a single dimension of regime type—flawed electoral competition—has prevented an examination of extra-electoral factors that are necessary for understanding how regimes are differently hybrid, why there is such immense variation in the outcome of elections and why these regimes are constantly in flux. Therefore, a key recommendation emerging from this review of the scholarship is that to achieve a more thorough, multi-dimensional assessment of hybrid regimes, further research ought to be driven by nested research designs in which qualitative and quantitative approaches can be used to advance mid-range theory building.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.013 |
| 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".