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Record W4220950360 · doi:10.3390/en15051889

Voluntary Simplicity and Green Buying Behavior: An Extended Framework

2022· article· en· W4220950360 on OpenAlexaff
Elena Druică, Călin Vâlsan, Andreea-Ionela Puiu

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsBishop's University
Fundersnot available
KeywordsFrugalitySimplicityConsumption (sociology)ConsumerismGreen consumptionProsperityContext (archaeology)TurnoverMarketingEconomicsBusinessAdvertisingMicroeconomicsEcologySociologyGeographySocial scienceEconomic growthMarket economy

Abstract

fetched live from OpenAlex

Green consumption is usually understood in the context of green consumption values and receptivity to green communication. Voluntary simplicity, a related yet distinct construct that relies on ecological responsibility, has not been included in the same framework. This paper bridges this gap and extends the original model to consider green consumption and voluntary simplicity in a unified structure. Based on a study conducted in Romania, it was found that 70% of the variation in buying behavior is explained by a combination of direct and mediated influences. The main takeaway is that any serious attempt to encourage responsible buying has to rely on a reduction in the absolute level of consumer demand. This result has far-reaching implications because the current paradigm of economic growth and prosperity is tributary to consumerism. The question is not how to avoid curtailing consumption and substitute green products for those harming the environment, but rather how to make voluntary frugality palatable.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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