Bibliographic record
Abstract
Declining access to fresh water is one of the twenty-first century’s most pressing environmental and human rights challenges, yet the struggle for water is not a new cause. The 8,800-kilometer border dividing Canada and the United States contains more than 20 percent of the world’s total freshwater resources, and Border Flows traces the century-long effort by Canada and the United States to manage and care for their ecologically and economically shared rivers and lakes. Ranging across the continent, from the Great Lakes to the Northwest Passage to the Salish Sea, the histories in Border Flows offer critical insights into the historical struggle to care for these vital waters. From multiple perspectives, the book reveals alternative paradigms in water history, law, and policy at scales from the local to the transnational. Students, concerned citizens, and policymakers alike will benefit from the lessons to be found along this critical international border. With contributions by Andrea Charron, Alice Cohen, Dave Dempsey, Jerry Dennis, Colin A.M. Duncan, Matthew Evenden, James W. Feldman, Noah D. Hall, Lynne Heasley, Nancy Langston, Frédéric Lasserre, Daniel Macfarlane, Andrew Marcille, Jeremy Mouat, Emma S. Norman, Peter Starr, Joseph E. Taylor III, and Graeme Wynn
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.908 | 0.908 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".