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Record W2587418916 · doi:10.1215/15476715-3718398

Constructing the Global History of the Knights of Labor

2017· article· en· W2587418916 on OpenAlexaboutno aff
Steven Parfitt

Bibliographic record

VenueLabor Studies in Working-Class History of the Americas · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsLabor historyWorld historyLong nineteenth centuryHistoryAmerican historyModern historyEconomic historyLabor relationsPolitical scienceLawAncient history

Abstract

fetched live from OpenAlex

The Knights of Labor became one of the great global working-class movements of the nineteenth century between 1880 and 1900, extending from New Zealand to Belgium, Scotland to South Africa. That story, however, has been told only in fragments, on local, regional, and national levels. No truly global history of the Knights of Labor yet exists. This article brings together what historians have so far uncovered of their activities outside Canada and the United States, provides an outline of what their global history might look like, and shows how that history enriches our understanding of national labor histories across the world and of the Knights themselves in their American home. Finally, this article addresses the wide range of historical topics that would benefit from such a global history, including labor and imperial history and the construction of gender and color lines on an international scale during the nineteenth century.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.471
Threshold uncertainty score0.947

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0110.020
Scholarly communication0.0090.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.052
GPT teacher head0.306
Teacher spread0.253 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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