Responsiveness in non-democratic regimes: The role of elections, legislatures and parties
Bibliographic record
Abstract
The purpose of this thesis is to understand how authoritarian and semi-authoritarian regimes can become responsive in the absence of free and fair elections, sometimes even more so than democracies. To address this issue, the thesis focuses on cases drawn from Southeast Asia. Many semi-authoritarian and authoritarian regimes in the region seem to be responsive, such as Vietnam, Malaysia and Singapore, while democracies have often failed to respond in a similar manner. To account for these surprising results, the argument put forth in this thesis is that the presence of nominally democratic institutions - elections, legislatures and parties - can contribute greatly to the responsiveness of non-democratic regimes. Such institutions make important information about a population's preferences available, and responsiveness therefore becomes easier, while they can also improve a regime's capacity to implement responsive policies. To contribute to responsiveness in this way, elections need to be semi-competitive, legislatures have to allow for some representation, and parties must be institutionalized. Under these conditions, nominally democratic institutions favor responsiveness in non-democratic regimes. Meanwhile, the absence of some of these requirements in Southeast Asian democracies helps account for their low levels of responsiveness. Since responsiveness is deeply linked to the well-being of the populations living under different regimes, it seems crucial to understand how non-democratic regimes can become responsive, while democratic regimes can fail to become so.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".