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Record W2961315213 · doi:10.3390/rel10070432

The ‘Greening’ of Christian Monasticism and the Future of Monastic Landscapes in North America

2019· article· en· W2961315213 on OpenAlexafffund
Jason M. Brown

Bibliographic record

VenueReligions · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Ecology, and Ethics
Canadian institutionsSimon Fraser University
FundersUniversity of British Columbia
KeywordsMonasticismSustenanceChristianityEnvironmental ethicsSociologyHistoryMetaphorDesert (philosophy)LamentArchaeologyLawPolitical sciencePhilosophyTheology

Abstract

fetched live from OpenAlex

Christian monasticism has an ancient land-based foundation. The desert fathers and later reform movements appealed to the land for sustenance, spiritual metaphor, and as a marker of authentic monastic identity. Contemporary Roman Catholic monastics with this history in mind, have actively engaged environmental discourse in ways that draw from their respective monastic lineages, a process sociologist Stephen Ellingson calls ‘bridging’. Though this study is of limited scope, this bridging between monastic lineages and environmental discourse could cautiously be identified with the broader phenomenon of the ‘greening’ of Christianity. Looking to the future, while the footprint of North American monastic communities is quite small, and their numbers are slowly declining, a variety of conservation-minded management schemes implemented since the 1990s by some communities suggests that the impact will remain for many decades to come.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.017
Scholarly communication0.0040.004
Open science0.0000.004
Research integrity0.0010.002
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 head0.007
GPT teacher head0.260
Teacher spread0.253 · 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 designQualitative
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

Citations4
Published2019
Admission routes2
Has abstractyes

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