Yea or Neigh? The Economics, Ethics, and Utility of the Horsemeat Filet
Bibliographic record
Abstract
While staying surprisingly low profile amongst the general populace, the issue of horse slaughter has become hotly contested in the last decade, evolving into a multifaceted controversy that intertwines questions regarding ethics, international commerce, and contemporary law and politics. Horses were slaughtered in the U.S. in United States Department of Agriculture (USDA) regulated plants until 2007, when an appropriations bill suspended funding for federal inspections of horsemeat. The U.S. was home to three domestic slaughterhouses - two in Texas and one in Illinois - that slaughtered an average of about 115,003 horses per year from 1990 to 2007. Currently, American horses are shipped by the thousands to Canada and Mexico for slaughter and processing or are sent to horse retirement at a horse hostel. Following a 2012 appropriations bill that technically allows horse slaughter in the U.S., strong efforts have been put forth to open equine slaughterhouses in the U.S., sparking controversy and causing groups against domestic equine slaughter to mobilize. Although polls show that eighty percent of Americans oppose slaughtering horses, many groups support its renewal on grounds of ensuring humane slaughter, decreasing horse abandonment, more efficiently allocating tax dollars, and improving economic efficiency. Anti-slaughter groups claim that horse slaughter can never be truly humane, is a betrayal to a useful companion animal, and that responsible breeding is the solution to horse abandonment. Though seemingly narrow and specific, North American horse slaughter affects a large plurality of stakeholders, involving the meat processing industry, farmers and ranchers, international horsemeat consumers, animal welfare groups, equestrian industry organizations, horse rescue programs, and taxpayers. Horse slaughter is an issue immersed in complexity and riddled with intricacies that make practical solutions difficult to find; like many problems in our world, all possible actions to remedy the issue carry significant tradeoffs, ensuring that some parties will suffer economic or ideological losses.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".