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Record W4281830180 · doi:10.1080/08941920.2022.2081999

“Steeped in Oil”: The Socio-Psychological Factors and Processes That Influence Community Members’ Attitudes toward Economic Diversification in an Oil and Gas-Producing Community

2022· article· en· W4281830180 on OpenAlexafffundabout
Kathleen Murphy, Kathryn Stone, Emma Stirling-Cameron, Lola Strand, Chaise Combs, Anam Khan, Michael Ungar

Bibliographic record

VenueSociety & Natural Resources · 2022
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of CalgaryDalhousie University
FundersCanadian Institutes of Health Research
KeywordsDiversification (marketing strategy)OptimismBoomPsychological resilienceBusinessMarketingSocial psychologyPsychologyEngineering

Abstract

fetched live from OpenAlex

Oil and gas-producing communities are threatened by a precarious oil market and global commitments to transition to a greener economy. Economic diversification has been proposed as a potential strategy for supporting the resilience of these communities amidst such challenges. We sought to explore community members’ attitudes toward the future of their small oil and gas-producing Canadian community to understand the socio-psychological factors and processes that influence their support for economic diversification and those which reinforce path dependency. This qualitative study involved interviews with 37 adults in the community, and a subset of 16 of those participants engaged in transect walks to further explore emerging themes. While the recent prolonged economic downturn prompted some participants’ willingness to diversify, the deeply ingrained culture and identity as an oil and gas town, the ‘golden handcuffs’ of the industry, and optimism for another boom, acted to reinforce path dependency.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.039
GPT teacher head0.268
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 designObservational
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

Citations6
Published2022
Admission routes3
Has abstractyes

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