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Wyrd Ecology

2025· book· en· W4410439176 on OpenAlexaboutno aff
Barbara Jane Davy

Bibliographic record

VenueOxford University Press eBooks · 2025
Typebook
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Abstract What if giving offerings matters not because of beliefs about appeasing the gods or the powers of nature, but because it gives people a sense of relatedness with the world that inspires them to care for it? Practices dismissed as outmoded superstition may play a critical role in shaping people’s sense of what matters and how we should comport ourselves. Making toasts, giving gifts, and making offerings matter to contemporary Heathens, and these practices contribute to their sense of how they should relate with others, including ecological relations. In Wyrd Ecology, Barbara Jane Davy presents an ethnography of contemporary Heathen gifting rituals in Canada that shows how such practices can contribute to the development of ecological conscience. Based on two years of participant observation and interviews with practitioners, Wyrd Ecology is well-grounded both in first-hand experience and the academic study of ritual, religion, and ecology. Giving gifts and expressing thanks in ritual in this community inspires in participants a sense of gratitude and a desire to give in turn. Among these Heathens this gratitude and felt sense of obligation extends beyond human relations to include all relations in the more than human world, or what Heathens understand as “wyrd.” Wyrd Ecology is the first ethnography of Heathen gifting rituals, the first study on contemporary Heathenry and environmentalism, and the first published in-depth study of Heathenry in Canada. It offers a unique interpretation of how ritual practices can inspire ethical and ecological behavior.

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)0.002
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 score0.490
Threshold uncertainty score0.975

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2810.059

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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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