Career trajectories, socialization, and backbench dissent in the British House of Commons
Bibliographic record
Abstract
Introduction There is much truth to Enoch Powell's maxim that all political lives end in failure. Indeed, the vast majority of MPs never attain a cabinet position. Even the lucky few who do stand a good chance of finishing their careers on the back bench, their time as ministers cut short by reshuffles, leadership changes, or simply age. This points to an inherent end-game problem with relying solely on advancement to maintain unity: all MPs realize that there comes a point where advancement is no longer forthcoming and once this point is reached, leaders must find other ways to elicit their MPs' loyalty. Discipline is an option, of course, but as the results of the previous chapter indicate, it is at most a stopgap measure. Certainly, those who take a sociological view of parliamentary politics argue that party leaders deal with these conditions instead by cultivating among their members a voluntary commitment to norms of loyalty and unity (e.g., Kornberg 1967, p. 134; Crowe 1986, p. 180; Jackson 1987, p. 55; Searing 1994). Thus over time MPs come to feel a sense of duty to the party and to internalize the costs of engaging in undesirable behaviour. In this chapter, I use data on British MPs' parliamentary careers and voting records to demonstrate in a quantitative fashion that socialization into norms of party loyalty limits MPs' propensity to dissent once their careers begin to decline.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".