Bibliographic record
Abstract
Record-breaking wildfires are striking the United States and Canada with troubling, increasing frequency. As wildfires know no borders, climate change-related wildfires will increasingly damage the ecosystems and economies of both nations unless they develop an efficient system of cooperation to deal with this shared threat. As the U.S. and Canada share similar cultures, legal systems, and interests in preserving their ecosystems and air qualities, these countries are in a unique position to share intelligence and resources to properly address the scale of these disasters. Scholars such as Madison Gaffney have noted the potential for current legislation to expand and treat aspects of these crises. But, a comprehensive defense against all of the risks posed by increasing wildfires will require more proactive coordination, including a willingness by either nation to be held accountable for their own disasters when the negative externalities of domestic wildfires begin to damage neighbors. As the United States and Canada share both the world’s largest land border and a robust, healthy diplomatic relationship, the development of a proactive, bilateral disaster response policy between the two nations could form the blueprint for other nations as the effects of climate change begin to compound. This paper attempts to outline the possible political and legal developments that would better protect both countries from shared environmental disasters.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".