Resilience in Times of Economic Boom and Bust: A Narrative Study of a Rural Population Dependent upon the Oil and Gas Industry
Bibliographic record
Abstract
How do residents of small towns that depend on oil and gas extraction or processing industries withstand economic boom and bust cycles? To answer this question, this article reports on a narrative analysis of residents' life stories gathered from 37 adults of a small town on the Canadian prairies dependent on the oil and gas industry, employing the theories of narrative inquiry and narrative identity. Participants aged 30 to 76 were interviewed and their experiences of living in an unstable economy that is dependent mostly on a single resource extraction industry were explored. Specifically, we asked participants about the effect of economic change on factors related to resilience like family interactions, work choices, educational pathways, and the quality of their social lives. Our analysis of adult narratives looked for patterns in the relationship between risk exposure, promotive and protective factors at multiple systemic levels (individual, relational, cultural), and functional outcomes such as individual coping, community cohesion, and social and economic sustainability. Results show that a strong identity, in particular expressions of personal agency, communion, and engagement in meaning making are contributing factors to adult resilience in a context of economic change. Our results also highlight how positive attitudes towards a better future may inadvertently undermine the need for residents of oil and gas-dependent towns to commit to economic diversification and other potential resilience-promoting strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".