Natural allies: environment, energy, and the history of US–Canada relations
Bibliographic record
Abstract
Nature has dictated relations between peoples and their states during much of human existence. But over time, and in a fog of politics and consumption, its power has slowly faded from the attention of the wealthy, who forgot how dependent humanity is upon nature for our continued survival. It took the sharp increase in existential language and environmental diplomacy in the 1980s to resuscitate our connection with nature. Because of the custodial failures of policy-makers, nature now demands renewed attention. As Daniel Macfarlane argues persuasively in Natural allies, market-driven ‘success’ in cross-border relationships often comes at the cost of the stability and security of the natural world. The book's twelve chapters shift between thematic and temporal topics, surveying the three-way relationship between Canada, the United States and the environment. It follows the evolution of negotiations over ‘wood and water—and a host of other resources’ (p. 3). It hopes to demonstrate how fully ‘understanding the history of Canada–United States relations requires comprehending the importance of environment and energy’ (p. 3). The author succeeds, but the book's complex structure (which bounces between thematic and time-bound chapters) can make it difficult to grasp important nuances in Macfarlane's findings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.003 |
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".