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Record W1489630543 · doi:10.1108/17557501011016271

Scrip, stores, and cash‐strapped cities

2010· article· en· W1489630543 on OpenAlexaff
Sarah Elvins

Bibliographic record

VenueJournal of Historical Research in Marketing · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCurrencyDilemmaValue (mathematics)PopularityBusinessCashGovernment (linguistics)MarketingGreat DepressionFace valueOriginalityNewspaperEconomicsAdvertisingFinanceMonetary economicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine retailer response to the use of alternative currency, or scrip, as an emergency measure during the Great Depression. Advocates of scrip argue that it would help recovery efforts, encouraging consumer spending and keeping dollars “at home” within the local community. Merchants face a dilemma, as they hope to use any means to increase sales, but are worried that they would be left holding a stack of worthless paper that they would not be able to pass on to their suppliers. Two cases of scrip in action in Chicago and Atlanta are contrasted. Design/methodology/approach This paper draws upon primary data sources including period newspapers from across the USA, business periodicals, archival materials from retailers and city councils, and government reports. Findings There is no uniform response to the use of scrip by merchants. Some retailers hope to use scrip to boost sales and encourage consumer loyalty, and even organized their own campaigns to use alternative currency. In other cases, retailers felt the risks of accepting scrip were too high. Without the participation of retailers, scrip schemes were doomed to failure. Originality/value In the early years of the Depression, alternative currency enjoyed a remarkable popularity across the USA. It is now known that scrip would not end the crisis, as boosters hoped, yet this episode reveals much about popular understandings of the economy, and the role of retailers in local communities.

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)0.001
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 score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0070.011
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.122
GPT teacher head0.317
Teacher spread0.195 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

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