Taxing Meat: Taking Responsibility for One’s Contribution to Antibiotic Resistance
Bibliographic record
Abstract
Antibiotic use in animal farming is one of the main drivers of antibiotic resistance both in animals and in humans. In this paper we propose that one feasible and fair way to address this problem is to tax animal products obtained with the use of antibiotics. We argue that such tax is supported both by (a) deontological arguments, which are based on the duty individuals have to compensate society for the antibiotic resistance to which they are contributing through consumption of animal products obtained with the use of antibiotics; and (b) a cost-benefit analysis of taxing such animal products and of using revenue from the tax to fund alternatives to use of antibiotics in animal farming. Finally, we argue that such a tax would be fair because individuals who consume animal products obtained with the use of antibiotics can be held morally responsible, i.e. blameworthy, for their contribution to antibiotic resistance, in spite of the fact that each individual contribution is imperceptible.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".