Bibliographic record
Abstract
During the Cold War, the United States and Canada embarked on an ambitious military construction project in the Arctic to protect North America from a northern Soviet attack. Comprised of sixty-three stations stretching across Alaska, Canada’s Arctic, Greenland, and Iceland, the Distant Early Warning (DEW) Line constitutes both the largest military exercise and waste remediation project in Canadian Arctic history. Despite the massive cleanup operation undertaken, the DEW Line’s waste legacy endures as a prominent and deeply rooted feature of Canada’s Arctic history. Drawing upon a rich historical, anthropological, military, political science, and environmental studies literature, this article explores waste as a key issue in the shifting narratives concerned with the modernization of the Canadian Arctic. While the DEW Line has been extensively analyzed in terms of its effects on the modernization of the Arctic, this article seeks to link Canadian sovereignty, security, resource exploitation, environmental stewardship, and Inuit self-determination directly to waste issues. As industrial activity and military exercises stand to significantly increase in the Arctic, I want to draw attention to the lessons of the DEW Line; that ”develop now; remediate later” incurs steep human health, environmental, financial, and political costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".