Bibliographic record
Abstract
In recent years the level of cohesion in parliamentary parties has continued to increase. Whereas in Congress the party leadership's capabilities to solve collective action problems and to reduce transaction costs have been in doubt, in parliamentary systems little seems to warrant such doubt. Therefore, the aim of this article is to trace the party leadership's particular capabilities to secure party unity in parliamentary systems by means of (i) contract design, (ii) screening and selection, (iii) monitoring and information requirements, and (iv) institutional checks. To the extent that these capabilities affect members differently, it is possible to sketch their contours on the basis of who the rebels are. It is apparent that discipline is not readily explained in terms of rewards and punishments. Factions and tendencies provide perhaps the most valid prediction for dissent in France and the United Kingdom, whereas in Belgium the extra-parliamentary party leadership and a detailed policy agreement have a strong impact on members' discipline.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.108 | 0.019 |
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".