Moving On, But Where? A Snapshot of Ontario Ministerial Staff Career Trajectories
Bibliographic record
Abstract
Abstract Despite their centrality to the success of government business, we know very little about political staff, including their contributions to the policy process, the ways in which they contribute to government decision‐making or their career trajectories. This research note examines the experience that political staff bring to their positions and where they find employment after leaving ministerial offices, using archived staff directories across four governments (NDP, Liberal and two Progressive Conservative administrations) in Ontario, Canada and cross‐referencing names using LinkedIn. In total, we explore career progression of 1,153 political staff who have employment information publicly available on LinkedIn. Many take these roles having remarkably diverse backgrounds, including finance, law, academia, business and, even, other roles in the public service. Upon leaving political service, some choose to join the non‐partisan ranks of public servants. Many leaving government head towards government relations firms to use their skills and experience to advance their careers. Most of these experiences are consistent across all three political parties that have held office in Ontario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".