EXAMINING POLICY CAPACITY INITIATIVES WITHIN CLIMATE CHANGE FRAMEWORKS IN THREE US AND CANADIAN SECTORS USING NVIVO CONTENT ANALYSIS
Bibliographic record
Abstract
The climate change narrative has changed from one of mitigation to one of adaptation. Governments around the world have created climate change frameworks which address how the country can better cope with the expected and unexpected changes due to global climate change. In an effort to do so, federal governments of Canada and the United States, as well as some provinces and states within these countries, have created detailed documents which outline what steps must be taken to adapt to these changes. However, not much is mentioned about how these steps will be translated in to policy, and how that policy will eventually be implemented. To examine the ability of governments to acknowledge and incorporate the plethora of scientific information to policy, consideration must be made for policy capacity. This report focuses on three sectors: water supply and demand; drought and flood planning; and forest and grassland ecosystems, and the word ‘capacity’ as related to nine different forms of policy capacity acknowledged in these frameworks. Qualitative content analysis using NVivo was carried out on fifty four frameworks and the results obtained show that there is a greater consideration for managerial capacity compared to analytical or political capacity. The data also indicated that although there were more Canadian frameworks which referred to policy capacity, the frameworks from the United States actually considered policy capacity to a greater degree.
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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 | 0.046 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".