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Record W4400228850 · doi:10.1086/726899

“Who Used to Run the UNIA Hall”: Black Canadian Women’s Leadership of Toronto Division 21, 1919–1939

2024· article· en· W4400228850 on OpenAlexaboutno aff
Melissa N. Shaw

Bibliographic record

VenueThe Journal of African American History · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsDivision (mathematics)Political scienceGender studiesSociologyMathematics

Abstract

fetched live from OpenAlex

This article argues that Black Canadian Garveyite women in Toronto were Universal Negro Improvement Association (UNIA) leaders and institution builders. During the interwar years, Black women implemented Garveyism as an intraracial grassroots practice in their local environs, harnessing Garveyite principles to tangibly improve the “standard of life” for Black people in their city. Through their Community Hall, Garveyite women ensured their division’s fiscal stability, fostered racial solidarity, and instilled essential intellectual skills and Garveyite values among youth. These leaders deweaponized racial stigma by building up Black people’s collective social esteem as an identifiable racialized group of Canadians. Drawing insights from diverse primary sources, including understudied oral history interviews, archival records, organizational records, and national and international newspapers, this article illuminates Black women’s understudied political labor. It unveils the wealth of Black history available when patriarchal and white supremacist norms are decentered. New analytical possibilities can be pursued when Black communities and Black liberation are recentered as the standard for evaluating Black activism intentions, motivations, methods, and practices.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.246
Teacher spread0.213 · 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
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
Published2024
Admission routes1
Has abstractyes

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