Family Resilience in a Resource‐Cursed Community Dependent on the Oil and Gas Industry
Bibliographic record
Abstract
The economic and social well-being of rural, "resource-cursed" communities can depend on the boom-bust cycles of a single industry like oil and gas. This study used a constructivist, inductive approach to identify the challenges placed on families in one such community and the processes that strengthen family resilience. Semi-structured interviews were conducted with 35 adult residents (30-76 years old, 19 women) from a community in Alberta, Canada, that has specialized in oil and gas extraction for 70 years and experienced its worst economic downturn while the study was underway. Results showed that many families have experienced an endless cycle of poor work-life balance and income instability throughout the economic cycle. Family life often lacked social cohesion as a consequence of demanding work schedules and economic pressures. Additional challenges were the perceived negative effects of rigid gender roles, substance abuse, family conflicts, and domestic violence. Crucial strengthening processes for family resilience were fundamental financial and living standard adaptations (e.g., living within or below one's economic means; having both spouses become earners), maintaining regular contact by having a flexible home routine, and mutually agreeing to change roles during busts (former earners take responsibility for caregiving and running of the household and vice versa). Alternatively, accepting economic volatility and its impact on normal family life processes were essential for family resilience. Findings suggest the need for clinicians to help families foster resilience in communities that depend on resource extraction industries with concurrent adaptations required by individuals, families, and socio-political and economic systems.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".