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Record W7124147433 · doi:10.1353/amp.2025.a979333

The Chicago Ledger : A Story Paper for the Country Custom

2025· article· en· W7124147433 on OpenAlexaboutno aff
Matthew Short

Bibliographic record

VenueAmerican periodicals · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAmerican History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsLedgerCognitive reframingCompetitor analysisQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

abstract: The Chicago Ledger appeared every week between 1873 and 1925, making it one of the longest-running story papers published in the United States. Rather than rely on the American News Company for distribution, like most of its competitors in New York and Philadelphia, it was sold directly to readers across the nation's farms, towns, and villages, where it was read in millions of country homes. Because it carried advertising and mostly circulated in rural areas, however, the Ledger has long been dismissed as a mere "advertising sheet" or "mail-order paper." This article examines the distribution, revenue, advertising, and editorial strategies used by the Ledger's publishers to argue for understanding it instead as a story paper for the country custom. This reframing makes it easier to appreciate all of the complex ways that story papers were read and sold, especially in non-urban markets, while opening up a wider conversations about weekly papers for the home, an important and long-neglected part of rural print culture.

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)0.007
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.006
Scholarly communication0.0120.005
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0130.003

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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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