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Record W4402509298 · doi:10.1093/jahist/jaae140

When Bad Men Combine: The Star Route Scandal and the Twilight of Gilded Age Politics

2024· article· en· W4402509298 on OpenAlexaff
Robert MacDougall

Bibliographic record

VenueJournal of American History · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsTwilightGilded AgePoliticsStar (game theory)HistoryArtPolitical scienceLawAstronomyPhysicsAstrophysics

Abstract

fetched live from OpenAlex

This crisply written book tells the story of the Star Route scandal, a sprawling case of corruption in the U.S. Postal Service that convulsed American politics in the early 1880s. Star Routes were mail delivery routes paid for by the postal service but operated by private contractors. Entrepreneurs bid for postal contracts, promising to deliver mail along a given route for a particular price. Thousands of these routes existed all over the country, with the longest, most potentially lucrative routes traversing the trans-Mississippi West. The situation was rife with graft opportunities. Speculators made fortunes by winning and then subcontracting multiple postal routes. Bribes and kickbacks to post office officials were widespread. Individuals could collude to drive out rival bidders or win contracts with phony bids, then default, letting confederates take over those routes at much-inflated prices. In 1881, the postal service paid one ring, surrounding former Arkansas senator Stephen W. Dorsey, over $100,000 to deliver just $582 worth of mail. That was for only one route, over the course of one year.

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)0.005
metaresearch head score (Gemma)0.010
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: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0140.018
Scholarly communication0.0140.009
Open science0.0010.005
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0150.002

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 head0.017
GPT teacher head0.201
Teacher spread0.184 · 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
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

Citations1
Published2024
Admission routes1
Has abstractno

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