Why we should think about democratic frontsliding as well as democratic backsliding
Bibliographic record
Abstract
For the past decade or so, we have been worrying about democratic backsliding – movement toward autocratic rule in nations that we thought were stably democratic. Our attention to backsliding may have distracted us, though, from another important phenomenon – front-sliding, so to speak. If backsliding is a move from democracy toward autocracy, frontsliding is a move from autocracy to democracy. Professors Dan Slater and Joseph Wong’s important book From Development to Democracy offers an elegant argument that sometimes autocrats themselves initiate movement toward democracy even when they are not facing imminent collapse. They show how dominant political parties in South Korea, Taiwan, and Japan gave up a seeming guarantee of remaining in power through continuing repression in order to remain in power through reasonably free and fair elections instead. Their argument, which I outline in Part ii, is that sometimes democracy occurs because the dominant party is strong rather than weak and on the verge of collapse. That opens up the possibility of similar frontsliding transformations in other authoritarian, autocratic, or quasi-autocratic nations, including Singapore and, most intriguingly, China. I examine this possibility in Part iii.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".