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Strikebreakers

2019· reference-entry· en· W4252808733 on OpenAlexaboutno aff
Stephen H. Norwood

Bibliographic record

VenueOxford Research Encyclopedia of American History · 2019
Typereference-entry
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsElitePopulationState (computer science)Political scienceGovernment (linguistics)Quarter (Canadian coin)Economic historyLawEngineeringHistorySociologyPoliticsDemography

Abstract

fetched live from OpenAlex

Abstract Strikebreakers have been drawn from many parts of the American population, most notably the permanently and seasonally unemployed and underemployed. Excluded from a vast range of occupations and shunned by many trade unions, African Americans constituted another potential pool of strikebreakers, especially during the early decades of the 20th century. During the first quarter of the 20th century, college students enthusiastically volunteered for strikebreaking, both because of their generally pro-business outlook and a desire to test their manhood in violent clashes. A wide array of private and government forces has suppressed strikes. Beginning in the late 19th century, private detective agencies supplied guards who protected company property against strikers, sometimes assaulting them. During the early 20th century, several firms emerged that supplied strikebreakers and guards at companies’ request, drawing on what amounted to private armies of thousands of men. The largest of these operated nationally. On many occasions the state itself intervened to break strikes. Like some strikebreaking firms, state militiamen deployed advanced weaponry against strikers and their sympathizers, including machine guns. Presidents Hayes and Cleveland called out federal troops to break the 1877 and 1894 interregional railroad strikes. In 1905, Pennsylvania established an elite mounted force to suppress coal miners’ strikes modeled on the British Constabulary patrols in Ireland. Corporations directly intervened to break strikes, building weapons arsenals, including large supplies of tear gas, that they distributed to police forces. They initiated “back to work” movements to destroy strikers’ morale and used their considerable influence with the media to propagandize in the press and on the radio. Corporations, of course, discharged strikers, often permanently. In the highly bureaucratized society of the late twentieth and early 21st century that stigmatized public displays of anger, management turned to new “union avoidance” firms to break strikes. These firms emphasized legal and psychological methods rather than violence. They advised employers on how to blur the line between management and labor, defame union leaders and activists, and sow discord among strikers.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, 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: Other · Consensus signal: Other
Teacher disagreement score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.352
Teacher spread0.301 · 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
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
Published2019
Admission routes1
Has abstractyes

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