Policy analysis in the federal government: conditions and renewal initiatives in the Trudeau era
Bibliographic record
Abstract
“We are a government that believes in science – and a government that believes that good scientific knowledge should inform decision-making.” (Justin Trudeau, 2016) Introduction In 2014, Liberal Party Leader Justin Trudeau said, “You can't run a government from one single person. What matters instead is that leadership be about gathering extraordinary individuals and getting the best out of them” (Bryden, 2014). Trudeau was responding to journalists’ questions about federal scientists who felt muzzled, and the need to improve policy capacity, open up government to evidence, and recreate the conditions of trust between citizens and government. The Liberals created expectations that the federal policy function would be called upon to be actively engaged, and used more than had been experienced under the previous Conservative government. In a 2016 interview, Clerk of the Privy Council Michael Wernick raised similar expectations: “All the important issues facing Canada are multifaceted that require collaboration, and we have to get better working across silos internally. One of the real challenges and opportunities the prime minister has given us is a lot more space to collaborate with people outside the public service” (May, 2016). Promises of public service mobilization, engagement, access to new tools and approaches, and greater openness have fuelled internal change toward enhancing the relevance of the policy community for decision-makers, the foundations for which were established over several governments. Significant changes are occurring within the federal Public Service with respect to the conditions under which policy is developed, implemented and assessed. It is challenging to get a sense of these changes, however, because the scope, depth and breadth of federal policy activities are diverse and expansive. The federal government has had to be nimble, strategic and smarter about its use of resources to address public problems (Klassen et al., 2017; Meier & Hill, 2007), and has had to adapt to a policy environment that is increasingly complex, connected and focused on governance rather than government (Frederickson, 2007). The complicated and disconnected, centrally orchestrated and one-time public management reform initiatives such as the Glassco Commission appear to be less attractive federally. Instead, there has been a shift to more targeted and discrete new public management (NPM) initiatives aimed at specific and incremental changes (Zussman, 2015).
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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 | 0.053 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.055 |
| Scholarly communication | 0.037 | 0.020 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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".