<i>Whales and Nations: Environmental Diplomacy on the High Seas</i> by Kurkpatrick Dorsey
Bibliographic record
Abstract
Whales and Nations: Environmental Diplomacy on High Seas by Kurkpatrick Dorsey. Foreword by William Cronon. Seattle and London, University of Washington Press, 2013. xxii, 365 pp. $34.95 US (cloth). In 1972 Reader's Digest published an excerpt from Canadian author Farley Mowat's A Whale for Killing, which told of his experience with a fin whale trapped in a cove near Burgeo, Newfoundland in 1967 and how local residents chose to torment whale rather than rescue it. As Kurkpatrick Dorsey observes, Mowat expressed and helped popularize sentiments that gained currency during 1970s and 1980s: people are fundamentally pretty ugly, but are pure. People could redeem themselves, though, by saving whales (p. 216). This understanding of was a new one. Throughout most of twentieth century, the dominant idea about was that they were strange and interesting, but they were first food and energy for humans (p. 10). Dorsey illustrates how, from early 1900s, whalers took advantage of new technologies that enabled them to hunt last great cetacean populations in Antarctic seas and reap profits from industrial demand for their oil. Even before World War I, some contemporaries had noted that whalers were following familiar cycle of finding a lucrative whale stock, rapidly increasing catches, and depleting resource until hunting was no longer profitable. A desire to prevent a recurrence of this pernicious pattern spurred international efforts to regulate whaling that are topic of Dorsey's rich and informative book. Drawing on an impressive array of archival sources from Canada, Great Britain, New Zealand, Norway, and United States, Dorsey reconstructs history of whaling diplomacy: its beginnings in early twentieth century, signing of international conventions in 1930s and creation of International Whaling Commission (IWC) in 1946, and eventual passage of a moratorium on commercial whaling after 1982. Dorsey organizes book's highly readable narrative around themes of sustainability, sovereignty, and science. Throughout twentieth century, as Dorsey shows, enlightened observers pursued agreements that would facilitate more rational exploitation of resources and make whaling sustainable. The goal was not to save whales, but to curtail hunting so that industry would not collapse. Whenever discussions centered on conserving for future use, however, those who wanted fewer restrictions generally won out. Like Dorsey's previous scholarship, Whales and Nations reminds us that ecological processes, including whales' migratory patterns, inevitably transcend human-constructed political boundaries. Defining such boundaries, moreover, proved especially difficult in maritime environments. …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.075 | 0.031 |
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".