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Record W2053083640 · doi:10.1080/13668790701329726

Death to Life: Towards My Green Burial

2007· article· en· W2053083640 on OpenAlexaff
Robert Feagan

Bibliographic record

VenueEthics Place & Environment · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Ecology, and Ethics
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsAstrobiologyEnvironmental ethicsBiologyPhilosophy

Abstract

fetched live from OpenAlex

This paper presents reflections on the author's death aspirations as they are informed by a set of earth-connection stories, environmental concepts, and modernist burial practices. This weave is meant to inspire further consideration on what is coming to be known as ‘green burial’. More precisely, this means an exploration of the author's earth-centred burial musings in association with the following themes: the meanings and historical trajectory of prevailing death and burial practices; ‘narratives’ of the human–earth life-cycle; relevant environmental ethics and place literature concepts; and lastly, some sense of the newly emerging practices and appeals to green burial—i.e. the normative and practical grounds for rethinking and working toward more environmentally sensitive burial practices. This weave of themes is instructive for posing green burial as evocative of a more comprehensive and spiritual ethos of connection, continuity, and responsibility. In this sense, rather than being seen as contrary or contentious, green burial may actually enable us to dispel some of the growing angst, uncertainty, and insensitivity often underlying prevailing burial practices, while contributing to an emerging environmental consciousness.

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)0.010
metaresearch head score (Gemma)0.011
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0220.033
Scholarly communication0.0090.007
Open science0.0020.013
Research integrity0.0040.013
Insufficient payload (model declined to judge)0.0030.001

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 head0.067
GPT teacher head0.356
Teacher spread0.288 · 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
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

Citations6
Published2007
Admission routes1
Has abstractyes

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