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Record W3028154812 · doi:10.1080/23800127.2020.1764255

Routes and roots: factors that drive labour mobility in Newfoundland and Labrador, Canada

2020· article· en· W3028154812 on OpenAlexafffundabout
Joshua Barrett

Bibliographic record

VenueApplied Mobilities · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSpouseSalaryWork (physics)GeographyDemographic economicsCapital (architecture)Capital cityLabour economicsEconomic geographySociologyPolitical scienceEconomicsEngineering

Abstract

fetched live from OpenAlex

In theory and practice, it has been noted that the length of daily commutes range from under 15 minutes to over one hour each way, often involving distances greater than 50 km. Both rural and urban residents make the decision to engage in extended daily commutes rather than relocate closer to their place of work. Using the Newfoundland and Labrador nickel processing sector as a case study, this paper identifies the factors that influence them to commute extended periods on a daily basis, including economic considerations, amenities, sense of belonging, and the nature of the commute. Mobile workers are more inclined to travel greater distances for work if the salary is an improvement from their current employment and if there are no opportunities available for work for their spouse. Further, an individual’s source community, or the community where they permanent reside, provides greater accessibility to location-based capital while off work. These factors influence the worker to engage in extended daily commuting despite the stress and the longer workdays associated with labour mobility.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.615

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 studies0.0000.001
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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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 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

Citations1
Published2020
Admission routes3
Has abstractyes

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