Bibliographic record
Abstract
Over the past couple of decades a number of new democracies have adopted anti-defection laws that penalize individual parliamentary deputies for changing their partisan affiliation during the inter-election period. The adoption of these measures seems reasonable in new democracies, where political parties are still weak and are not yet “parliamentary fit” (Sartori 1997), which is an important prerequisite for the proper functioning of parliamentary government. However, it is much more puzzling to find that alone among the more established democracies, India and Israel have also chosen to do so. In this article, we argue that anti-defection laws are adopted in rare circumstances in established democracies: freely elected legislators do not easily choose to give up their freedom of movement in the legislature. In order to uncover the conditions that may lead to such reforms we compare the experience of three democracies: India and Israel, where such reforms were successfully adopted, as well as Canada, where repeated attempts at such reforms have failed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".