Sinking Mink: An Argument for Ending the Mink Industry in Nova Scotia
Bibliographic record
Abstract
Nova Scotia produces more mink pelts than any other province, but its fur farming industry imposes costs disproportionate to its benefits. These costs include the substantial financial aid given to mink farms, the toxic algae blooms in some lakes, the frequent spread of viruses from mink to humans and wildlife, the regular and wasteful mass culls of diseased mink, and the animal suffering caused by captivity, neglect, and abuse. Federal and provincial legislation does not and cannot provide meaningful protection to mink because of innate species characteristics and the cruelties inherent in the fur industry. Due to the association between mink farming and the spread of COVID-19, British Columbia plans to prohibit the practice; Nova Scotia should do the same.\nLa Nouvelle-Écosse produit plus de fourrures de vison que toute autre province, mais son industrie de la fourrure impose des coûts disproportionnés par rapport à ses avantages. Ces coûts comprennent l’aide financière substantielle accordée aux fermes de visons, la prolifération d’algues toxiques dans certains lacs, la propagation fréquente de virus du vison aux humains et aux animaux sauvages, l’abattage massif régulier et inutile des visons malades et la souffrance animale causée par la mise en captivité, la négligence et les mauvais traitements. Les lois fédérales et provinciales n’offrent pas et ne peuvent pas offrir une protection significative au vison en raison des caractéristiques innées de l’espèce et des cruautés inhérentes à l’industrie de la fourrure. En raison de l’association entre l’élevage de visons et la propagation de la COVID-19, la Colombie-Britannique prévoit d’interdire cette pratique. La Nouvelle-Écosse devrait faire de même.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".