Representational Counterbalancing: The Case of Cabinet Ministers and Parliamentary Secretaries in Canada
Bibliographic record
Abstract
This paper argues that when heads of government appoint politicians to government teams, they focus on a particular range of the appointees’ representational attributes and construct selection pools for other team positions with an eye toward counterbalancing the appointees’ salient representational attributes. Previous research has investigated horizontal counterbalancing, which takes place within teams whose members have roughly equal status (e.g., cabinets). This paper suggests that there is additional value to be gained by examining vertical counterbalancing, which occurs when selectors appoint subordinates whose attributes counterbalance those of their superiors. Empirically, the paper spotlights teams of federal cabinet ministers and parliamentary secretaries in Canada from 1963-2021. It demonstrates that prime ministers have used parliamentary secretary appointments to counterbalance—in order—the provincial/territorial, linguistic, gender, and ethnic attributes of the ministers they serve. It shows that caucus characteristics, partisanship, and (to some extent) prime ministers’ personal identities condition their counterbalancing behaviours.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.041 | 0.010 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".