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Record W4286628824 · doi:10.3390/rel13070625

Shifting Epistemologies, Shifting Our Stories—Where Might We Find Hope for a World on the Brink of Climate Catastrophe?

2022· article· en· W4286628824 on OpenAlexaboutno aff
Mary E. Hess

Bibliographic record

VenueReligions · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Ecology, and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsResistance (ecology)NarrativeEmbodied cognitionMythologyLiteracyDesert (philosophy)SociologyAestheticsEnvironmental ethicsGender studiesHistoryEpistemologyLiteratureEcologyArtPedagogyPhilosophy

Abstract

fetched live from OpenAlex

In the early 1990s, David Orr wrote about the epistemological myths of North American culture, and offered ecological literacy as a form of resistance. In the same decade, Parker Palmer confronted dominant epistemologies in religious institutions, and retrieved early Christian frames by way of resistance. One was writing through the lens of environmental science, and one through the lens of the desert mothers and fathers of Christian history. Neither acknowledged the First Nations, Metis and Inuit epistemologies which offered similarly contesting frames. It may be too late, yet even in a moment of climate catastrophe there is hope that shifting our forms of knowing can invite pedagogical practices that transform our communities. This essay will articulate the congruence between these disparate and diverse stances as sacred ground within which to root embodied, theologically astute pedagogies for the 21st century. Several pragmatic exercises that have emerged as fruitful for learners seeking to embody compelling counter narratives are also offered.

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)0.021
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.053
Scholarly communication0.0170.034
Open science0.0020.012
Research integrity0.0050.012
Insufficient payload (model declined to judge)0.0050.001

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 head0.062
GPT teacher head0.344
Teacher spread0.281 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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