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Record W2755130001 · doi:10.1093/jcr/ucx100

Creating a Consumable Past: How Memory Making Shapes Marketization

2017· article· en· W2755130001 on OpenAlexaff
Katja H. Brunk, Markus Giesler, Benjamin J. Hartmann

Bibliographic record

VenueJournal of Consumer Research · 2017
Typearticle
Languageen
FieldPsychology
TopicNostalgia and Consumer Behavior
Canadian institutionsYork University
Fundersnot available
KeywordsMarketizationHegemonyCapitalismRedressSocialismConsumption (sociology)State socialismPolitical economySociologyEconomic systemAcquiescencePoliticsCommunismEconomicsMarket economyPolitical scienceSocial scienceChinaLaw

Abstract

fetched live from OpenAlex

Abstract Consumer researchers tend to equate successful marketization—the transition from a socialist to a capitalist economy—with the consensual acquiescence to an idealized definition of the socialist past. For this reason, little research has examined how memories about socialism influence marketization over time. To redress this gap, we bring prior consumer research on commercial mythmaking and popular memory to bear on an in-depth analysis of the marketization of the former German Democratic Republic. We find that, owing to a progressive sequence of conflicts between commercialized memories of socialism promoted by marketing agents and countermemories advocating socialism as a political alternative, definitions of the past, and by extension, capitalism’s hegemony are subject to ongoing contestation and change. Our theoretical framework of hegemonic memory making explains relationships among consumption, memory making, and market systems that have not been recognized by prior research on consumption and nostalgia.

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)0.008
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 score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.160
GPT teacher head0.468
Teacher spread0.308 · 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

Citations68
Published2017
Admission routes1
Has abstractyes

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