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Record W4220991928 · doi:10.1177/00221856221088153

Working towards a green job?: Autoworkers, climate change and the role of collective identity in union renewal

2022· article· en· W4220991928 on OpenAlexafffundabout
Kori Allan, Joanna L. Robinson

Bibliographic record

VenueJournal of Industrial Relations · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSolidarityPolitical economyCollective identityPolitical scienceContext (archaeology)EnvironmentalismIdentity (music)Collective actionNegotiationNeoliberalism (international relations)SociologyEconomic systemPoliticsEconomicsLaw

Abstract

fetched live from OpenAlex

This article examines the important, yet under-examined, issue of green workforce development and industrial relations and the role of unions and workers in shaping a transition to a green economy. Based on interviews with labour leaders and rank-and-file workers in the auto manufacturing sector in Ontario, Canada, this article interrogates how environmentalism and climate change potentially construct a sense of purpose among heterogenous union members, particularly in the context of decreasing union power, de-industrialization and neoliberalism. In order to understand how climate change can shape union purpose, we investigate how a diverse range of union members – beyond leaders – understand climate change and the appropriate strategies to address it and how this sustains or hinders collective identity within the union. We argue that understanding internal differences in collective identities is key for unions to start to rebuild power resources. Our research demonstrates that future union success and solidarity among workers might be dependent on the ability of unions to recognize and negotiate multiple collective identities. By incorporating innovations into the union, a more flexible and multi-dimensional collective identity regarding labour environmentalism could be built and sustained.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.066
GPT teacher head0.312
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations23
Published2022
Admission routes3
Has abstractyes

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