Patterns of backbench dissent in four Westminster parliamentary systems, 1945–2005
Bibliographic record
Abstract
An elite body can never satisfy all the ambitions of its members; if talents and ambitions are always more numerous than places, there are bound to be many who cannot rise quickly enough by making use of the body's privileges and who seek fast promotion by attacking them. (de Tocqueville 1848/1965, p. 265) Introduction The LEADS model links dissent to a number of variables: the MP's electoral security, career advancement, and the like. These observable implications are developed within the context of an individual-level model of behaviour, but many can be tested equally well at the aggregate level. If, for example, advancement suppresses an MP's propensity to dissent, then parties in which opportunities for advancement are relatively abundant should experience less dissent than parties in which such opportunities are scarce. There is also scope at the aggregate level to test arguments about dissent that are not directly addressed by the model. The confidence convention stands out in this regard: it does not play a role in the model (by assumption, the MP's vote is not critical to the survival of the government), but it is a central feature of parliamentary government, and one that might be expected to influence the level of dissent that parties experience. Indeed, the impact of these sorts of institutional variables is often better studied at the aggregate level, where there is typically more room for institutional variation than at the individual level.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".