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Record W2524526468 · doi:10.29173/cjs28288

Battling Blind Spots: Hours of Service Regulations and Contentious Mobilities in the BC-Based Long Haul Trucking Industry

2016· article· en· W2524526468 on OpenAlexaffvenue
Amie McLean

Bibliographic record

VenueThe Canadian Journal of Sociology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMobilitiesBlameTruckEquity (law)PoliticsService (business)SociologyPower (physics)BusinessPolitical scienceMarketingLawEngineeringSocial science

Abstract

fetched live from OpenAlex

This article explores arenas of contention in which long haul truckers’ workplace mobilities are enmeshed. I critically analyze the grounded implications of Hours of Service (HoS) regulations, a primary regulatory mechanism for addressing the dangers posed by truck driver fatigue. I argue that HoS regulations enforce a neoliberal individualization of responsibility that fails to account for industry power dynamics or truckers’ lived experiences of labour mobility. These dynamics add to concerns about the potential exploitation of migrant truck drivers, including through the Temporary Foreign Worker Program. Inasmuch as they fail to address the classed, gendered and racialized dynamics of trucking mobilities, HoS regulations are implicated in perpetuating hierarchies of power in the industry. As such, they are inadequate and – in contextually specific ways – counterproductive to promoting employment equity or overall public safety. These issues are particularly evident in the contentious politics of blame concerning heavy truck-involved collisions.

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)0.003
metaresearch head score (Gemma)0.008
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.460

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.021
Scholarly communication0.0080.003
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.047
GPT teacher head0.300
Teacher spread0.252 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

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