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Record W4319990335 · doi:10.5751/es-13776-280110

Collective responsibility and environmental caretaking: toward an ecological care ethic with evidence from Bhutan

2023· article· en· W4319990335 on OpenAlexvenueno aff
Elizabeth Allison

Bibliographic record

VenueEcology and Society · 2023
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousEnvironmental ethicsFlourishingBuddhismSustainabilitySociologyNatural resourceEnvironmental stewardshipSovereigntyPolitical scienceSocioeconomicsEcologyGeographyLawPsychologySocial psychology

Abstract

fetched live from OpenAlex

Attention to environmental caretaking practices of Indigenous, traditional, and rural societies is an important strategy for Indigenous sovereignty and self-determination, as well as for greater ecological sustainability and resilience. Rural practices of caring for the eco-social commons in Himalayan Bhutan demonstrate an implicit care ethic. Mahayana Buddhism and indigenous animism blend to create distinctive attitudes and practices of environmental caretaking displayed in rural relationships with forests, mountains, and water bodies that influence community-based natural resource management. Elements of an eco-social care ethic became even more vivid in the nation's response to the Covid-19 pandemic. Bhutan's response was among the world's most successful, forestalling any deaths at all for the first nine months of the pandemic and limiting deaths to nine total as the pandemic entered its third year in March 2022. Bhutanese Buddhist values and practices parallel the care ethics articulated by Western moral theorists, providing a contemporary example of caring for the common good and alternative pathways toward flourishing futures.

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)0.006
metaresearch head score (Gemma)0.009
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 score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.010
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.045
GPT teacher head0.324
Teacher spread0.279 · 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

Citations17
Published2023
Admission routes1
Has abstractyes

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