Operationalizing ‘Policy Capacity’: A Case Study of Climate Change Adaptation in Canadian Finance Agencies
Bibliographic record
Abstract
Although a widely used term in the literature, much of what we know about “policy capacity” in government is limited to anecdotal evidence. Policy scholars have not systematically investigated the ability of policy professionals to provide good advice in relation to new policy challenges; indeed many are skeptical that policy capacity (understood as the potential for “evidence based policy learning”) is an important driver of policy change in the first place. Despite these empirical and theoretical problems, governments remain committed to improving policy capacity in the pursuit of better public policy. This paper offers some preliminary observations on the difficulty of studying and operationalizing policy capacity through an examination of the finance sector in relation to climate change adaptation; part of a large collaborative SSHRC CEI project. Drawing on the existing literature on Canadian finance policymaking dynamics, a survey of policy professionals in the area, and an illustrative case study, the paper makes two claims. It suggests that viewing capacity as involving both the cognitive skills of professionals (or “analytical capacity”), and the institutional arrangements in which policy research is conducted (or “governance arrangements”), is a useful starting point. However, as the findings in this paper highlight, if capacity is the ability to provide effective advice in relation to specific problems, then the nature of the problem itself (how “wicked” or otherwise it might be) will also impact capacity.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.031 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".