Beyond the Arctic: the strategic and national security implications of climate change for Canada.
Bibliographic record
Abstract
Within the last thirty years, linkages between climate change, inter or intra-state conflicts, and national security have become more apparent. Three main adverse impacts of climate change impinge on environmental security: resource scarcity, the increase in frequency and intensity of natural hazards, and human migration. Additionally, current projections regarding global population growth, climbing temperatures, rising sea levels, and political instability suggest that the impact of climate on global and national security will likely continue to feed the conflict cycle and escalate tensions worldwide. Given this context, this monograph will investigate the impacts of global climate change on the environmental and national security of Canada and the repercussions for the Canadian Armed Forces beyond the Arctic. This monograph is divided into four sections. The first section will provide a summary of the current state of affairs regarding climate change as a global phenomenon influencing environmental security. The second section will trace the linkages between the current environment and the ramifications for Canada as a nation. The third section will focus on Canada's national security policy in a climate-changing world. The last section will conclude the monograph and offer observations and implications for the contemporary and future of the CAF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".