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Record W2912209887 · doi:10.1093/jcr/ucz004

Consumer Movements and Value Regimes: Fighting Food Waste in Germany by Building Alternative Object Pathways

2019· article· en· W2912209887 on OpenAlexaff
Johanna Gollnhofer, Henri Weijo, John W. Schouten

Bibliographic record

VenueJournal of Consumer Research · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsValue (mathematics)Order (exchange)Object (grammar)MarketingCorporate governanceBusinessConstruct (python library)EconomicsManagement

Abstract

fetched live from OpenAlex

Abstract Consumer movements strive to change markets when those markets produce value outcomes that conflict with consumers’ higher-order values. Prior studies argue that consumer movements primarily seek to challenge these value outcomes by championing alternative higher-order values or by pressuring institutions to change market governance mechanisms. Building on and refining theorization on value regimes, this study illuminates a new type of consumer movement strategy where consumers collaborate to construct alternative object pathways. The study draws from ethnographic fieldwork in the German retail food sector and shows how building alternative object pathways allowed a consumer movement to mitigate the value regime’s excessive production of food waste. The revised value regime theorization offers a new and more holistic way of understanding and contextualizing how and where consumer movements mobilize for change. It also provides a new tool for understanding systemic value creation and the role of consumers in such processes.

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)0.002
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 score0.005
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.010
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.001
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 head0.046
GPT teacher head0.315
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 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

Citations110
Published2019
Admission routes1
Has abstractyes

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