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Record W3206454646 · doi:10.1177/15327086211049703

Decluttering the Pandemic: Marie Kondo, Minimalism, and the “Joy” of Waste

2021· article· en· W3206454646 on OpenAlexaff
Jennifer A. Sandlin, Jason Wallin

Bibliographic record

VenueCulture Studies &#x2194 Critical Methodologies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMinimalism (technical communication)AestheticsCapitalismSociologyProsperityPolitical scienceArtPoliticsLaw

Abstract

fetched live from OpenAlex

Born largely from discourses on environmental sustainability, the contemporary minimalist movement has produced a new relationship to consumer objects. Where the accumulation of objects once conferred the status of wealth and prosperity under capitalism, minimalism aims to rethink the object as a spiritual extension of our inner lives. This is nowhere as evident than in the writing of Marie Kondo, whose teachings on “joyous” decluttering has enraptured a new class of consumers. Yet, for as much as contemporary thinking on minimalism figures in the image of eco-conscious neo-spirituality, this essay aims to demonstrate the relationship of minimalism to waste. For as much as the decluttering of our private spaces signals to the values of self-control and discipline, it also inadvertently intensifies a relationship to objects in which things that fail to “spark joy” become consigned to the garbage dumps and landfills that today swell with the abject accumulation of consumer society. For as much as the fashion of minimalism gestures to the aspirations of anti-consumerism, it is concomitantly the positive condition upon which the overflowing possessions of a Western consumer class are fated to become trash.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.050
Scholarly communication0.0070.009
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.205
GPT teacher head0.475
Teacher spread0.270 · 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
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

Citations29
Published2021
Admission routes1
Has abstractyes

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