Assessing the Promise and Performance of Agencies in the Government of Canada
Bibliographic record
Abstract
Abstract Canada has not escaped trends in most liberal democracies with the rapid growth of agencies created by government to deliver public goods, often justified on elements of their mandate—service delivery, adjudication of disputes, regulatory oversight, among others—benefiting from an arm's-length relationship to the government of the day. Yet Canadian studies of this phenomenon remain mostly absent from the robust comparative literature theorizing and documenting the emergence of widespread “agencification” and its relationship to performance. This article draws on the Government of Canada's Public Service Employee Survey (PSES) microdata from 2017 to test key hypotheses advanced by proponents of agencification, specifically that agencies are more innovative, autonomous and efficient public organizations. We discover that those working in agencies generally report less climate of innovation and less work autonomy than those working in departments, though some types of agencies—namely regulatory and parliamentary ones—defy these trends.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".