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Record W3166729539 · doi:10.1080/23800127.2021.1935060

Construction work and the mobility imperative: changing rhythms along uncertain paths

2021· article· en· W3166729539 on OpenAlexafffundabout
Lachlan Barber, Barbara Neis

Bibliographic record

VenueApplied Mobilities · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMobilitiesNegotiationApprenticeshipSociologyWork (physics)Economic geographyGeographySocial scienceEngineering

Abstract

fetched live from OpenAlex

This article considers experiences of rhythmic change related to employment-related geographical mobilities in parts of the Canadian construction industry. Drawing on Lefebvrian rhythmanalysis and aspects of time-geography, we consider how workers and their loved ones negotiate changes in space-time patterns across careers in industrial construction, especially work at projects tied to resource development and extraction. Data are derived from in-depth career history interviews conducted with workers in the Canadian province of Newfoundland and Labrador between 2014 and 2018. Three “career path” cases illustrate mobile rhythms of differently positioned workers from their entry into construction to their career stage at the point of the interview, ranging from apprenticeship through mid-career journeyed to retirement. These workers pursue training and jobs involving variable mobilities between home and work across shifting locations. We contribute to recent efforts to highlight the compatibility of rhythmanalysis with an expanded, feminist, biographical approach to time-geography, and the applicability of such an approach for the applied study of mobilities. We also respond to recent calls to study experiences of rhythmic change in the lives of mobile and migrant workers. Findings reveal that changes in mobile rhythms may be small and incremental, as in the case of schedule or rotation adjustments, or sweeping and large scale, as in the case of shifts from working locally to working in distant locations amidst the disruptions caused by the COVID-19 pandemic. Experiences of disruption and responses to change are personal and familial, conditioned by social positions and subjectivities.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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 designTheoretical or conceptual
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

Citations5
Published2021
Admission routes3
Has abstractyes

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