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Record W4390752563 · doi:10.1353/mhr.2023.a917087

Prohibition’s Proving Ground: Cops, Cars, & Rumrunners in the Toledo-Detroit-Windsor Corridor by Joseph Boggs (review)

2023· article· en· W4390752563 on OpenAlexaboutno aff
Adam Pole

Bibliographic record

VenueMichigan Historical Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAmerican History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsWindsorLawArchaeologyGeographyPolitical science

Abstract

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Reviewed by: Prohibition’s Proving Ground: Cops, Cars, & Rumrunners in the Toledo-Detroit-Windsor Corridor by Joseph Boggs Adam Pole Joseph Boggs. Prohibition’s Proving Ground: Cops, Cars, & Rumrunners in the Toledo-Detroit-Windsor Corridor. Toledo: The University of Toledo Press, 2020. Pp. 237. Bibliography. Index. Images. Notes. Paperback: $24.95. Borders present opportunities for criminals and challenges for police. They can also draw the attention of historians. Joseph Boggs’ study of Prohibition in the Great Lakes region seeks to answer the question: how did the region’s car culture shape bootlegging and prohibition enforcement? This book will appeal to those interested in the Great Lakes region, policing, automobiles, and the Prohibition Era. The geographic focus of the book is what Boggs calls the Toledo-Detroit-Windsor Corridor, which is the area from Toledo to Detroit, along Lake Erie and the Detroit River, as well as Windsor, Ontario, across the Detroit River. His study is thus both regional and international, encompassing Lucas County in northwestern Ohio, Monroe and Wayne Counties in southeastern Michigan, and Essex County in southwestern Ontario. This corridor, the epicenter of the automobile world, presented challenges to police enforcing dry regulations. One such challenge was the Detroit River itself, which—particularly when frozen in winter—created a porous international border that was difficult and dangerous to patrol and intercept rumrunners driving across the river. The second challenge, and the focus of Boggs’ study, was the ways in which the region’s car culture shaped the distribution of illegal alcohol. The corridor was the car capital of North America, with the continent’s highest per capita car ownership rates. This widespread car ownership meant large numbers of individuals and gangs used their private automobiles to move the contraband. Tens of thousands of cars on the road made it difficult to effectively enforce the prohibition measures, particularly where police forces were chronically underfunded. The region’s dry laws predated the nationwide prohibition of the Volstead Era (1920–1933). By mid-1919, the entire Toledo-Detroit-Windsor Corridor was dry as provincial and state laws came into effect. Windsor was first, with Prohibition enacted in 1916 and extended in 1918. Detroit was next in May 1918 (becoming one of the country’s largest dry cities), and Ohio followed a year later in May 1919. The staggered implementation of prohibition laws in the region created predictable movements of goods and people: when Detroit turned dry in 1918, thirsty consumers and rumrunners simply drove to northwest Ohio, which was still wet. [End Page 89] Police forces were underfunded in the Toledo-Detroit-Windsor region. In the initial months of dry enforcement, the Michigan constabulary had only one patrol car, one supply truck, and thirty horses. Enterprising officers used the confiscated rumrunners’ cars for patrols until forbidden to do so by the state attorney general. The police eventually responded to the actions and methods of the criminals. The increased number of shootings and carjackings by rumrunners led the Michigan constabulary to employ an armored car, known as “Little Bertha,” that had armor plating and long-range guns. Additionally, Michigan’s 1919 Lewis Act permitted the constabulary to confiscate vehicles involved in rumrunning and, significantly, made prohibition violations felonies instead of misdemeanors—giving law enforcement greater legal cover in using their firearms in policing incidents. Adam Pole University of Windsor Copyright © 2023 Historical Society of Michigan

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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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.020
GPT teacher head0.238
Teacher spread0.218 · 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.

Study designNot applicable
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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