TRANSIENCY, FLY-IN-FLY-OUT WORKERS, AND SUSTAINABILITY: PERCEPTIONS FROM WITHIN A RESOURCE-BASED COMMUNITY
Bibliographic record
Abstract
The dynamics of the modern workforce required for large industrial development has changed over the last several decades.More specifically, many companies based in oil and gas extraction are opting to adopt a fly-in-fly-out (FIFO) workforce model, in particular those based on the extraction and production of oil and gas, in an attempt to minimize infrastructure costs and alter the cyclic boom/bust nature associated with resource extraction.Employing semi-structured interviews with key informants from Fort McMurray, Alberta, perhaps the most notorious resource-based community in Canadian history, this paper details how residents perceive the FIFO workers and what impacts this new employment strategy may have on their community.The primary findings indicate that while it is necessary to have access a large workforce, the use of FIFO workers negatively impacts the local community in several ways.First, the use of FIFO workers not only reduces the interaction that employees have with the nearby community, but alters their perception of that community.Second, FIFO workers access local infrastructure (e.g.healthcare) but do not support further development through taxes and discretionary income.Third, the transiency of FIFO workers affects place-attachment and long-term sustainability of the region.This research contributes to existing literature on resourcebased communities, sustainable urban development, and FIFO employment through use of a Canadian case-study that illustrates local experiences of the impacts of a relatively new employment model that has the potential to significantly impact resource-based communities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".