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Record W4413485701 · doi:10.1111/dial.70009

A Spiritual Dimension of Human–Animal Relations?

2025· article· en· W4413485701 on OpenAlexaff
Per Torp Sangild, Pui Him Ip

Bibliographic record

VenueDialog · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsTrinity College
Fundersnot available
KeywordsDimension (graph theory)Human DimensionEpistemologySociologyPhilosophyPsychologyPolitical scienceMathematicsPure mathematicsLawHuman rights

Abstract

fetched live from OpenAlex

ABSTRACT A close relationship with nature and animals is widely acknowledged to support human physical, social, and mental health, but is more seldom considered relevant for human spirituality. Using theological resources, this paper argues that some human–animal relations may possess spiritual qualities. While classical Christian (Augustinian and Thomistic) theologies give limited support for said spiritual dimensions, Franciscan traditions, modern incarnation theology, and ecotheology provide inspiration to articulate spiritual elements in some human–animal relationships. The latter theologies resonate well with the holistic nature views in specific Eastern religious traditions (Hinduism and Daoism). Together, these resources may serve as interreligious and cross‐cultural inspirations for a human relationship to animals that incorporates spiritual elements of biological, social, and divine interconnectedness. While our arguments may relate mostly to companion animals, they have relevance for human relations to all animals in the more‐than‐human nature. Empirical studies can be done in the future to test if specific human–animal relationships affect human spirituality. If confirmed, a Christian theology of animals that is attentive to potential spiritual effects of human–animal relationships will support health and flourishing in a world where humans are often alienated from nature and 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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.013
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.037
GPT teacher head0.373
Teacher spread0.336 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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