“Steeped in Oil”: The Socio-Psychological Factors and Processes That Influence Community Members’ Attitudes toward Economic Diversification in an Oil and Gas-Producing Community
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".