From Algorithms to Arctic Ice: AI's Role in Climate Adaptation from Ottawa to Oslo
Bibliographic record
Abstract
This study contributes uniquely to the understanding of artificial intelligence (AI) policy development in smaller economies, with a focus on Canada and Norway. It examines the ethical, economic, and environmental aspects of AI regulation and presents insights into how these nations navigate AI policies in the context of global AI superpowers. Positioned adjacent to larger economies—the United States and the European Union (EU) for Canada and Norway, respectively—these countries provide a specialized perspective on the intersection of national AI strategies, international influences, and climate change imperatives. The comparative analysis uncovers key differences and similarities, leading to actionable policy recommendations. Qualitative research methods are employed to critically evaluate policy documents, expert commentaries and case studies, explaining the objectives, strategies, and approaches of the two nations. The findings indicate that both have made progress in applying AI to climate action, yet they encounter challenges such as policy coherence, rapid technological changes, and the influence of larger geopolitical forces. Recent innovations like OpenAI's ChatGPT are examined for their potential impact on forthcoming regulatory frameworks. Although rapid policy and technological shifts may soon date some aspects of this study, it lays a foundational groundwork for future research aimed at bridging the gaps between technology, policy, ethics, and global environmental imperatives.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".