Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".