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Record W4411010253 · doi:10.1016/j.erss.2025.104146

How do oil and gas workers cope with a changing economy? Economic vulnerability among rural Canadians in the oil and gas sector

2025· article· en· W4411010253 on OpenAlexafffundabout
Lesley Hodge, Matt Ormandy, Alexa Ferdinands, Geraldine Cahill, Maria Mayan

Bibliographic record

VenueEnergy Research & Social Science · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsRocky Mountain CollegeAthabasca UniversityProvincial Laboratory of Public HealthAlberta Hospital Edmonton
FundersKillam Trusts
KeywordsVulnerability (computing)Fossil fuelBusinessRural economyEconomic sectorEconomicsRural areaEconomyPolitical scienceEngineeringWaste management

Abstract

fetched live from OpenAlex

Significant changes in the energy sector are needed to remedy the growing threat of climate change. Oil and gas workers are a critical social actor in this problem; however, further research is needed to understand the nuances of their perspectives on transitioning to renewable energies. We generated qualitative data with oil and gas workers in a Canadian oil capital to explore their perceptions about economic diversity and answer the research question: how do oil and gas workers cope with a changing economy? Semi-structured interviews were analyzed using thematic analysis and a critical posthumanism theoretical orientation. We found two interrelated themes that answer our research question and are pertinent to a key concept in critical posthumanism (‘becoming’): insulating from economic volatility and departing from inter-generational identities. A concern for children and families' futures was evident in both themes. Our findings suggest a need to overcome polarization associated with the fossil fuel industry and instead, focus on local economic support for oil and gas workers who foresee a departure from the industry that is deeply entangled with their communities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.013
GPT teacher head0.248
Teacher spread0.236 · 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 designOther design
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
Published2025
Admission routes3
Has abstractyes

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