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Record W4393202288 · doi:10.3390/ani14071005

The Long Road from Religious and Ethical Traditions to Welfare of Invertebrates

2024· article· en· W4393202288 on OpenAlexaff
Jennifer A. Mather

Bibliographic record

VenueAnimals · 2024
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsEnvironmental ethicsIgnoranceSentienceIndigenousAnthropocentrismHinduismBuddhismAnimal ethicsHarmAnimal welfareEcologySociologyPhilosophyAestheticsEpistemologyPsychologySocial psychologyBiologyReligious studiesTheology

Abstract

fetched live from OpenAlex

Ethical behaviour tends to lead to the welfare consideration of animals, but much less so for invertebrates. Indigenous tradition often valued all animals as having an important role in life on the planet, a practical application of modern ecology. The Judaeo-Christian-Islamic tradition postulated 'man' as having dominion over all of Earth, resulting in anthropocentrism and careless practices. In contrast, the Buddhist/Hindu belief in rebirth leads to ahisma, or doing no harm. In the face of capitalist systems, practice does not necessarily follow these beliefs, especially in the 'shepherding' of domestic animals. Only Jainist beliefs value the lives of all invertebrates. Philosophers are often divorced from the physiological reality of the animals they muse about, and science's traditions of objectivity and the simplest possible explanation of behaviour led to ignorance of invertebrates' abilities. Ninety-seven percent of animals on the planet are invertebrates. We have a long way to go to provide moral standing and welfare consideration, which is consistent with the new information about the sentience of some of these animals.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.035
Scholarly communication0.0040.004
Open science0.0000.004
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.033
GPT teacher head0.331
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2024
Admission routes1
Has abstractyes

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