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Record W791097837 · doi:10.4000/belphegor.593

Visual Fictions and the U.S. Treasury Courtesans: Images of 19th-Century Female Clerks in the Illustrated Press

2015· article· en· W791097837 on OpenAlexvenueno aff
Midori V. Green

Bibliographic record

VenueBelphégor · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAmerican History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsTreasuryWhite (mutation)BureaucracyGovernment (linguistics)NewspaperDevaluationHistoryPolitical scienceEconomic historyGender studiesLawSociologyEconomicsPolitics

Abstract

fetched live from OpenAlex

During the Civil War, the United States Treasury began hiring female clerks to work within its departments, a decision that would lead to the federal government becoming, according to historian Cindy Sondik Aron, “the first large, sexually integrated, white-collar bureaucracy in America.” By 1864, a congressional committee had already begun looking into accusations that some of the women were mistresses of government officials and that the Treasury Department had been turned into a harem. A second scandal, this time playing out in the press in 1869, renewed the old imagery of the harem. This article looks at how female Treasury clerks were portrayed in the two most popular illustrated weeklies at the time, Frank Leslie’s Illustrated Newspaper and Harper’s Weekly, as well as The Days’ Doings and Harper’s Bazar, and the use of misleading visual tropes that called women’s characters into question. The employment of these pernicious “visual fictions” aided in the creation of stereotypes of working women that continued well into the twentieth century, which, in turn, contributed to the devaluation of white-collar women and their work.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.238
Teacher spread0.211 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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