Bibliographic record
Abstract
The roots of the Canadian harp seal hunt can be traced to the 16th Century. But in the mid-20th century, opposition to the commercial hunt became widespread after television images of seal pups being killed with clubs on the pack ice off the coast of Newfoundland were broadcast around the world. International conservation groups, animal welfare groups, animal rights groups, and foreign governments have been calling for the Canadian government to end the commercial seal hunt on the grounds that it is inhumane and that harvest levels are unsustainable. The Canadian government defends the traditional practices of hunting harp seals, argues that seal pelts are an important source of income for sealers, and insists that the killing methods are humane and that harvest levels are sustainable. Emotions run high on both sides of the debate. The purpose of this paper is to evaluate whether or not there is a purely economic argument for ending Canada's commercial seal hunt. The paper finds that the benefits of ending the commercial hunt exceed the costs, but not unequivocally. However, the paper argues there should be a higher criterion--the Pareto criterion--for ending the commercial hunt; that is the hunt should end only if winners compensate the losers. The paper goes on to argue that an effective way to satisfy this criterion is to introduce a system of individual transferable quotas (ITQs) and let the market reveal the value of the commercial seal hunt. In addition to many other advantages such as improving the safety and efficiency of the hunt, the ITQ market could provide a mechanism by which those willing to pay to end the hunt could do so directly to sealers thereby ensuring that the hunt is scaled back or ultimately ended only when it is economically efficient and unambiguously welfare-improving.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".