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Record W2764074973 · doi:10.2495/sdp170091

TRANSIENCY, FLY-IN-FLY-OUT WORKERS, AND SUSTAINABILITY: PERCEPTIONS FROM WITHIN A RESOURCE-BASED COMMUNITY

2017· article· en· W2764074973 on OpenAlexaffabout
Leith Deacon, Jacob Papineau, Trina Lamanes

Bibliographic record

VenueWIT transactions on ecology and the environment · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOn the flySustainabilityResource (disambiguation)PerceptionComputer scienceEcologyPsychologyBiology

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.320
Teacher spread0.289 · 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; both teacher heads agree on what is shown here.

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

Citations7
Published2017
Admission routes2
Has abstractyes

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