Bibliographic record
Abstract
This appendix examines the impact of demotion on an MP's parliamentary career. I estimate a model of ministerial career prospects with data from the 1972 cohort of Canadian Liberal and Conservative MPs. The sampling frame spans 1972 to 1995, the year by which the last MP elected in 1972 left the Commons. The dependent variable is a dummy variable that notes whether an MP enjoyed some time as a junior or senior minister or opposition critic. The key independent variable is a demotion variable that measures the number of ranks (if any) that an MP was demoted during their career. (If MPs were demoted twice, the data were based on their careers up until that first demotion.) MacDonald's (1987) work on parliamentary careers stressed that MPs' professional ambition was the single best predictor of upward mobility, and it is quite reasonable to imagine that ambitious MPs are more likely to shrug off a setback and start a second climb up the parliamentary career ladder than their less ambitious colleagues. If ambition is a critical control variable, it is also a difficult one to measure. MacDonald used surveys to assess how professionally ambitious British MPs were, but I do not have the luxury of these sorts of data. Instead, I use the MP's career trajectory to construct a proxy for ambition.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 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".