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Record W1528510396 · doi:10.3390/rel6030794

The Roman Catholic Tradition in Conversation with Thomas Berry’s Fourfold Wisdom

2015· article· en· W1528510396 on OpenAlexaff
Simon Appolloni

Bibliographic record

VenueReligions · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Ecology, and Ethics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConversationEnvironmental ethicsIndigenousAnthropoceneEcological crisisPerspective (graphical)SociologyEcologyEpistemologyPhilosophyArt

Abstract

fetched live from OpenAlex

Taking the threatening anthropogenic global environmental destruction—the anthropocene—as a starting point, this paper examines the Catholic tradition, which has remained relatively indifferent to this looming crisis, asking what might help it to change its focus from a decided human ecology to one that counts the human as an integral part of the larger natural ecology. Thomas Berry, whose teachings underlie this work, suggests that since the tradition has grown out of a cosmological perspective that places the human being at the center of ethical deliberations and separate from the natural world, it needs to rely on other Earth-centered and ecological expressions to help Catholics to discover more harmonious avenues of being on Earth, which he describes as a fourfold wisdom—the wisdoms of indigenous peoples, classical traditions, women, and science. Through a critical weaving of these wisdoms into a conversation with the Catholic tradition, this article examines the efficacy of the fourfold wisdom to transform the tradition into a more Earth-honoring expression. While this work concludes that the fourfold wisdom is effecting change where engaged, it also reflects on the challenges and opportunities this engagement faces in light of current realities within the Catholic tradition.

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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0130.029
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.316
Teacher spread0.259 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2015
Admission routes1
Has abstractyes

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