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Record W3024167957 · doi:10.7202/1068740ar

Transportation and temp agency work: Risks and opportunities for migrant workers

2020· article· en· W3024167957 on OpenAlexaffvenueabout
Jill Hanley, Manuel Salamanca Cardona, Mostafa Henaway, Lindsay Larios, Nuha Dwaikat Shaer, Sonia Ben Soltane, Paul Eid

Bibliographic record

VenueCahiers de géographie du Québec · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversité du Québec à MontréalUniversity of OttawaConcordia UniversityWilfrid Laurier UniversityMcGill University
Fundersnot available
KeywordsAgency (philosophy)Work (physics)Gateway (web page)Argument (complex analysis)BusinessLabour economicsIntervention (counseling)Job securityControl (management)Migrant workersPublic relationsMarketingEconomic growthEconomicsPolitical scienceEngineeringSociologyManagementComputer sciencePsychology

Abstract

fetched live from OpenAlex

Access to transportation has long been recognized as key to people’s employment outcomes. Being able to get to work affordably, safely and on time makes all the difference in terms of job security and satisfaction. Recently, the rise of temporary placement agencies, especially as a gateway into the labour market for many newcomers to Canada, raises new questions. In this article, we present the findings of a 3-year longitudinal study that followed 42 (im)migrant temp agency workers in 5 sectors to explore the trajectory of their experiences. We analyze the role of transportation within their employment and make the argument that access to transportation—and especially the lack of it—is an important factor in temp agencies’ control and exploitation of workers. At the same time, those seeking to help workers can look into their work commute as a place of intervention.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.098
GPT teacher head0.327
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2020
Admission routes3
Has abstractyes

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