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Record W2520925909 · doi:10.1177/0263774x16668171

New mobile realities in mature staples-dependent resource regions: Local governments and work camps

2016· article· en· W2520925909 on OpenAlexaffabout
Laura Ryser, Greg Halseth, Sean Markey, Marleen Morris

Bibliographic record

VenueEnvironment and Planning C Politics and Space · 2016
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsSimon Fraser UniversityUniversity of Northern British Columbia
Fundersnot available
KeywordsNuclear decommissioningWork (physics)WorkforceBusinessResource (disambiguation)Local governmentRevenueGovernment (linguistics)Economic growthEnvironmental planningPublic administrationPolitical scienceEngineeringFinanceEconomicsGeography

Abstract

fetched live from OpenAlex

In resource-dependent regions, work camps have reshaped workforce recruitment and retention strategies and relationships with communities as they are increasingly deployed within municipal boundaries. This has prompted important, but controversial, questions about local government policies and regulations guiding workforce accommodations to support rapid growth in resource regions. Even as mobile workforces become more prevalent, however, few researchers have examined the development, operations, and decommissioning of these work camps. Drawing upon the experiences of local governments in Australia, Canada, Scotland, and the United States, this research examines how mobile workforces are shaping the opportunities and challenges of planning and local government operations through work camps integrated in mature staples-dependent resource regions. Our findings reveal that while some industries have taken the initiative to implement new protocols and operating procedures to improve the quality and safety of work camp environments, local governments have underdeveloped policy tools and capacities to guide the development, operations, and decommissioning of work camps. Failure to purposefully address work camps as a land-use issue, however, is significant for mature staples-dependent towns that ultimately fail to capture taxation revenues while incurring the accelerating costs for infrastructure and services associated with large mobile workforces.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2016
Admission routes2
Has abstractyes

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