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Record W2768131033 · doi:10.1177/0008429817733141

Nimrod

2017· article· en· W2768131033 on OpenAlexvenueno aff
Karl A. Hoerig

Bibliographic record

VenueStudies in Religion/Sciences Religieuses · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Ecology, and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationCasualHistorySociologyEnvironmental ethicsGeographyPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

Each fall from 1984 to 2007 a group of Lutheran pastors in Texas gathered at the ranch of another pastor to hunt white-tailed deer during the opening week of the annual hunting season. Called “Nimrod” after the ancient Babylonian king identified in the Bible as “a mighty hunter before the Lord” (Genesis 10:9), also an acronym for “November Invitational Ministerial Recreational Outdoor Diversion,” the event provided opportunities for recreation and fellowship for active and retired clergy, centered around the hunt. To the casual observer hunting is not an immediately obvious pastime to bring Christian ministers together. This ethnographic study examines the place of hunting within Christian theology and explores how the annual deer hunting retreat in fact created an ideal opportunity for clergy to escape from the social constraints of their professional lives while engaging in the deeply meaningful practice of harvesting wild game.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.391
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3910.175

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 head0.237
GPT teacher head0.511
Teacher spread0.274 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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