MétaCan
Menu
Back to cohort
Record W1517730819

Showing Agency on the Margins: African American Railway Workers in the South and Their Unions, 1917–1930

2013· article· en· W1517730819 on OpenAlexaff
Joseph Kelly

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsAthabasca University
Fundersnot available
KeywordsWhite (mutation)Agency (philosophy)Government (linguistics)New DealAfrican americanPolitical scienceWork (physics)Spanish Civil WarWorld War IIWhite supremacyLawRacismPublic administrationSociologyPoliticsEngineering
DOInot available

Abstract

fetched live from OpenAlex

During World War I and the 1920s, African American trainmen throughout the South took advantage of federal administrative bodies that had set anti-discrimination rules to challenge racist employers and white trainmen alike. After the war, white workers insisted that African Americans be relegated to porter jobs. White employers demanded that African American workers who continued to work as brakemen and flagmen, as they had during the war, accept lower wages for such skilled work than their white counterparts were paid. The federal government preferred to turn a blind eye to racial discrimination against African American workers in the period after federal control of the railways ended. Despite this concerted attack from all sides on their rights, unions of African American trainmen continued their fight, with some success, before federal administrative tribunals as well as the courts to retain skilled positions and receive the same pay as their white equivalents. Only the devastation of rail jobs in the 1930s largely destroyed the African American trainmen’s wartime gains.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0390.010
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0080.001

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 head0.022
GPT teacher head0.226
Teacher spread0.204 · 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 designQualitative
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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueProject Muse (Johns Hopkins University)Same topicRace, History, and American SocietyFrench-language works237,207