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Record W4400768657 · doi:10.3998/ergo.6167

Kantian Animal Moral Psychology: Empirical Markers for Animal Morality

2024· article· en· W4400768657 on OpenAlexafffund
Erik Nelson

Bibliographic record

VenueErgo an Open Access Journal of Philosophy · 2024
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsDalhousie University
FundersUniversity of TorontoUniversity of AlbertaDalhousie UniversityDartmouth College
KeywordsMoralityEpistemologySocial psychologyPhilosophyEnvironmental ethicsPsychology

Abstract

fetched live from OpenAlex

I argue that a Kantian inspired investigation into animal morality is both a plausible and coherent research program. To show that such an investigation is possible, I argue that philosophers, such as Korsgaard, who argue that reason demarcates nonhuman animals from the domain of moral beings, are equivocating in their use of the term ‘rationality.’ Kant certainly regards rationality as necessary for moral responsibility from a practical standpoint, but his distinction between the noumenal and phenomenal means that he can only establish it as a marker for morality from a theoretical standpoint. This means that when it comes to evaluating the moral capabilities of others, rationality can be neither necessary nor sufficient for morality, leaving open the possibility of other empirical markers for moral responsibility. I argue that the higher faculties, character, implicit knowledge of universality, and antecedent practical pleasures (which provide a way to distinguish between morally motivated behaviour and other types of socially motivated behaviour) can all serve as empirical markers for morality. There is empirical evidence that at least some animals have conceptual capabilities and therefore the empirical marker of the higher faculties. In addition, there is suggestive evidence that merits further investigation for the other three markers. While this will not provide a definitive answer on whether animals are capable of acting morally, it will provide a Kantian outlook that can be used to evaluate empirical and philosophical work on animal morality.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.458
GPT teacher head0.504
Teacher spread0.047 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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