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Record W4410488756 · doi:10.1111/anti.70032

Power, Narrative, and Fossil Fuels: Meaning‐Making and the Co‐Optation of Workers’ Struggle

2025· article· en· W4410488756 on OpenAlexfundaboutno aff
Megan Egler, Cheryl Morse

Bibliographic record

VenueAntipode · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsAlienationNarrativeMeaning (existential)Power (physics)SociologyWork (physics)Capital (architecture)Fossil fuelPolitical economyPolitical scienceLawHistoryArchaeologyEpistemologyEcology

Abstract

fetched live from OpenAlex

Abstract Discursive power operates through narrative to shape subjective perceptions of meaningful work within capitalist societies, where work as employment is required for survival. This article theorises the relationship between labour alienation and the adoption of narratives that create meaning while rationalising and defending the class structure. It presents an empirical example of how discursive power interacts with the material and structural realities of fossil fuel workers in the formation of extractive subjectivities. We asked workers from two of North America's most prominent regions of historical fossil fuel extraction—northern Alberta, Canada, and West Texas, United States—to narrate their experiences and perspectives. Drawing on their words, we explore the resonance between workers’ accounts of alienation, the rationalisations they articulate, and the narratives circulated by fossil fuel capital. Our findings have implications for those working toward more just and ecological societies within the polarised contexts of energy and climate.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.236
Teacher spread0.228 · 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 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
Published2025
Admission routes2
Has abstractyes

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