Bibliographic record
Abstract
One health suggests that human and animal health are comparable, but in practice, the concept aligns with the principles of public health ethics. One health ethics, as such, appears to eschew connotations of equality for the natural world. A theory of agency revises that anthropocentric assumption. This article begins with a critique of environmental dualism: the idea that human culture and nature are separate social realms, thus justifying public health as a (unifying) purpose. In response, this article argues that, first, a neuroethics of one health might equally regard humans and (some) animals, which have comparable mental states, as rational agents. Second, rational agency should ground our moral connections to nature in terms of the egalitarian interests we have (as coinhabitants) in the health of the planet. While this article makes a moderate case for interspecific rights (as the first argument asserts), neuroscience is unlikely for now to change how most public institutions regard nonhuman animals in practice. However, the second argument asserts that rational agency is also grounds for philosophical environmentalism. One health ethics, therefore, is a theory of equality and connects culture to nature, and, as such, is a separate, but coextensive approach to that of public health.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".