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Record W4309474688 · doi:10.1017/s0963180122000044

One Health Requires a Theory of Agency

2022· article· en· W4309474688 on OpenAlexaff
Benjamin Capps

Bibliographic record

VenueCambridge Quarterly of Healthcare Ethics · 2022
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsDalhousie University
FundersUniversity of Cambridge
KeywordsArgument (complex analysis)Agency (philosophy)DualismAnthropocentrismEnvironmentalismEnvironmental ethicsSociologyPublic healthEpistemologyNormative ethicsRationalitySocial sciencePolitical scienceLawPhilosophyPoliticsBiologyMedicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.251
GPT teacher head0.372
Teacher spread0.121 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2022
Admission routes1
Has abstractyes

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