The Bering Sea Fur Seal Arbitration—The Lawyers Try, But Fail, to Save the Seals
Bibliographic record
Abstract
Abstract In 1893, the United States and the United Kingdom resorted to international arbitration to resolve a bitter dispute triggered by US arrests of Canadian vessels hunting fur seals at sea in the North Pacific. The fashion industry’s demand for seal furs had led to extensive pelagic hunting that threatened the seal herd’s survival. The United States claimed rights to protect the seals, which summered and gave birth to their young on the US-owned Pribilof Islands, partly based on misunderstandings regarding historic Russian rights thought to have been acquired with the US purchase of Alaska. The arbitration tribunal rejected these US claims to jurisdiction and its invitation to develop nineteenth-century international law to protect a threatened species. While the tribunal ruled for the United Kingdom, it also exercised an unusual power to devise its own regime intended to preserve the seals. The tribunal’s regime, proclaimed without a sound scientific foundation, failed and pelagic sealing increased. As the seal population neared collapse, Japan, Russia, the United Kingdom, and the United States agreed in 1911 on the North Pacific Fur Seal Convention, a landmark in the development of international law for the protection of living resources.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".