The Quest for Coherence in Climate Actions: The Case for Québec's Climate Strategy
Bibliographic record
Abstract
ABSTRACT Studies on climate policy coherence often focus on policy components as the essential element of success by examining their objectives, instruments, and implementation practices. However, while some studies have demonstrated that it is essential to evaluate the programs or actions that translate policies into success, few have focused specifically on program coherence. This research uses monitoring sheets for programs ( N = 177) funded under the Québec Climate Change Action Plan (CCAP) 2013–2020 to perform a comprehensive analysis of program coherence, combining relational content analysis and social network analysis. Findings suggest a failure to achieve the action plan and provide pertinent shortcomings and gaps related to the program's objectives and indicators, including challenges with collaboration and coordination. Given the complexity and cross‐cutting nature of climate issues, this study contributes to the literature on policy coherence and argues in favor of program coherence for a better design and assessment of the effectiveness of climate policies and programs. Moreover, through an integrative framework for policy coherence, the study suggests adding public programs to the existing policy components for policy coherence assessment. While relying on effective collaboration and coordination, implications for practitioners include a rigorous use of various program management tools used to design, monitor, and implement public programs.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".