Subsystem Structures, Shifting Mandates and Policy Capacity: Assessing Canada’s Ability to Adapt to Climate Change
Bibliographic record
Abstract
Adapting to climate change requires governments to design and implement policies capable of dealing with long-term problems. This poses significant policy design and implementation challenges since policies must also be multilevel and multi-sectoral in nature given the cross-sectoral and international character of climate change issues. Responsive policy-making on climate change issues thus requires both sophisticated policy analysis as well as an institutional structure which allows problems to be dealt with in a way which corresponds with changing organizational mandates, resources and network structures. Designing such policies requires matching policy analytical resources in relevant government departments and agencies with new and expanded mandates, a process which is not always necessarily successful. This introductory article presents the framework utilized in a collaborative study of climate change adaptation capacity in four Canadian policy sectors (agriculture, finance, infrastructure, and transportation) and one US case (the energy sector in Colorado). The study framework and subsequent analysis examine policy from a three-level perspective including (1) the macro nature of the subsystem involved, (2) the meso level of the organization or leadagency in charge of the issue and (3) the micro level nature of policy work being undertaken in each sector.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".