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Record W3160512461 · doi:10.13001/jwcs.v4i2.6231

Gender and Working-Class Identity in Deindustrializing Sudbury, Ontario

2019· article· en· W3160512461 on OpenAlexaboutno aff
Adam D.K. King

Bibliographic record

VenueJournal of Working-Class Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDeindustrializationSolidarityWorking classRestructuringIdentity (music)Gender studiesEconomic restructuringSocial classPolitical scienceClass (philosophy)SociologyEconomic growthEconomyPoliticsEconomicsLaw

Abstract

fetched live from OpenAlex

In this article I explore the making of a gendered working-class identity among a sample of male nickel miners in Sudbury, Ontario, Canada. Through 26 oral history interviews conducted between January 2015 and July 2018 with current and retired miners (ages 26 to 74), I analyze how the industrial relations framework and social relations of the postwar period shaped – and continue to shape – a masculinized working-class identity. I then examine the ways in which economic restructuring and the partial deindustrialization of Sudbury’s mines have affected workers’ ideas about gender and class. I argue that, amid growing precarious employment in both the mining industry and the regional economy more broadly, the male workers in this study continue to gender their class identities, which limits attempts to build working-class solidarity in a labor market now largely characterized by feminized service sector employment.

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 score0.045
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0230.008
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.158
GPT teacher head0.299
Teacher spread0.141 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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