Trends in the performance of arms‐length agencies in the Government of Canada
Bibliographic record
Abstract
Abstract Canada mirrors developments in most countries with the growth of government agencies created to deliver public goods—whether it is service delivery, adjudication of disputes, regulatory oversight, enforcement activities—purported to benefit from an arms‐length relationship to cabinet. There is a robust comparative literature documenting the “agencification” of the state, yet Canadian studies remain mostly absent. This article draws on the Government of Canada's Public Service Employee Survey (PSES) microdata from 2017, 2011, 2005, and 1999 to test key hypotheses advanced by proponents of agencification, specifically that agencies are more innovative, autonomous, and efficient public organizations. We find that those working in enforcement agencies exhibit few of the purported advantages of agencification. We also observe that in recent years regulatory, adjudicative, and parliamentary agencies consistently surpass conventional department organizations on these metrics. Future research avenues are proposed to explore how governance and oversight reforms may explain this shift.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".