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Record W2895293503 · doi:10.1177/0308518x18801025

The geography of skill: Mobility and exclusionary unionism in Canada’s north

2018· article· en· W2895293503 on OpenAlexafffundabout
Suzanne Mills

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousLegitimacyCompetition (biology)RacismPolitical scienceWork (physics)PoliticsGeographyLawEngineering

Abstract

fetched live from OpenAlex

This paper explores the spatial politics of racism and inter-worker competition through a case study of Indigenous employment during the construction of the Voisey’s Bay mine in northern Labrador. Over the course of construction, the building and construction trades unions (BCTUs) sought to restrict the hiring of local Inuit and Innu workers by challenging the legitimacy of place-based entitlements to work. Inuit and Innu workers had preferential access to employment as a result of unresolved land claims and the ensuing Impact and Benefit Agreements (IBA) between the Voisey’s Bay Nickel Company and both the Innu Nation and the Labrador Inuit Association. IBA provisions that local Inuit and Innu be hired preferentially ran counter to the unions’ organizational structures and cultures, which privileged worker mobility and skill. The BCTUs used the geographic incompatibility between the scale of Indigenous claims and that of construction worker organization to justify a competitive approach to unionism and to veil racist portrayals of Innu and Inuit workers. By drawing out the relation between skill, racism and beliefs about entitlements to work, this paper explores how workers selectively use place-based and mobile identities to participate in inter-worker competition, reifying colonial patterns of labour mobility and labour market segmentation.

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 score0.258
Threshold uncertainty score0.557

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 studies0.0010.000
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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations15
Published2018
Admission routes3
Has abstractyes

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