The Changing Role of Consultants in Canadian Policy Analysis
Bibliographic record
Abstract
Abstract This article explores the Canadian government's trend toward outsourcing policy analytical and advisory services over the 1980s and 1990s. We test three (not mutually exclusive) hypotheses about why such policy consulting has increased. A combination of New Public Management values and knowledge expansion are shown to drive this trend. In the late 1990s, the federal government significantly decreased the number of in-house operational and administrative support workers. These reductions enabled the government to contract out more while a managerial and administrative cadre remained in place to manage the contracts. The main implication is that contracting out analytical and advisory services does not necessarily lower costs, but does aid government in pursuing other objectives. The growth in Canadian policy consulting corresponded with a stabilization of public service personnel expenditures, a focus on leaner government, and an ability to rely on outside specialists for objective, expert advice.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".