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Record W2922623934 · doi:10.1177/0731121419836966

Eco-habitus or Eco-powerlessness? Examining Environmental Concern across Social Class

2019· article· en· W2922623934 on OpenAlexaff
Emily Huddart Kennedy, Jennifer E. Givens

Bibliographic record

VenueSociological Perspectives · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of British Columbia
FundersWashington State University
KeywordsHabitusSocial psychologyAssociation (psychology)PsychologyVariation (astronomy)Class (philosophy)Social classSociologyQualitative researchFace (sociological concept)Political scienceCultural capitalSocial scienceEpistemology

Abstract

fetched live from OpenAlex

Recent evidence of an association between status and eco-friendly practices invites examination of environmental concern across social class. Analyzing interview data from 64 socioeconomically diverse residents of Washington state, we observe variation in orientation to the environment across social class. High-status participants embody an eco-habitus—a sense that being “green” is good and also achievable. Lower-status participants express “eco-powerlessness”—fear and uncertainty in the face of environmental issues and a sense that one’s daily actions have little bearing on broader issues. We suggest that, among our participants, existing measures of environmental concern capture variation in their alignment with high-status preferences for environmental actions and in self-evaluations of their role in mitigating environmental problems. Our research contributes to a more culturally nuanced understanding of environmental concern by using qualitative data to explicate the association between social class and perceived self-efficacy to enact socioecological change in an era of consumer-based solutions to ecological crises.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.316
Teacher spread0.285 · 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 designObservational
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

Citations98
Published2019
Admission routes1
Has abstractyes

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