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Record W3122152795 · doi:10.3968/11968

Three Main Stages of the Development of Animal Ethics

2020· article· en· W3122152795 on OpenAlexvenueno aff
Limin Wu, Xin Zhou

Bibliographic record

VenueCanadian social science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsAnthropocentrismAnimal ethicsSentienceEnvironmental ethicsAnimal rightsConsciousnessFeelingValue (mathematics)Agency (philosophy)Human animalSociologyAnimal welfareDialogical selfDignityNon-humanPsychologySocial psychologyEpistemologyLawPolitical scienceSocial sciencePhilosophyEcologyBiologyLivestock

Abstract

fetched live from OpenAlex

The reflections on the relationship between animals and humans can date back to ancient times. Many philosophers and thinkers ponder on animals and human-animal relationship, and present their perceptions of animal ethics, which exert great impact on their followers and influence people’s attitude towards animals. According to the extent of the recognition and realization of animals as well as interspecies relationship, the paper divides the development of animal ethics into three main stages. The first stage is full of anthropocentric prejudices against animals, according to which animals are deemed as inferior to human beings while their consciousness, minds, and emotion are denied. The second stage witnesses an awakening to animals’ sentience, consciousness, and feelings, a call for animals’ liberation, and a defense for animals’ rights. There’s increasing attention to animals’ living condition, pains, needs and wants, together with their value, esteem, moral agency, and ethical rights. The third stage attaches importance to interspecies interconnection and interaction. There’re more and more record of, researches into, and calls for human-animal interactions, in which humans’ attention to and care for animals are underscored. A dialogical interspecies ethics is advocated for cross-species interactions, which subverts the former anthropocentric and dualistic concept of human-animal relationship.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.264
Teacher spread0.205 · 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 designObservational
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

Citations0
Published2020
Admission routes1
Has abstractyes

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