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Record W1526330910 · doi:10.1080/13639080.2015.1074664

Mining aboriginal labour: examining capital reconversion strategies occurring on the risk management field

2015· article· en· W1526330910 on OpenAlexafffundabout
Andrew P. Hodgkins

Bibliographic record

VenueJournal of Education and Work · 2015
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVocational educationSociologyApprenticeshipGeneral partnershipSocial capitalHabitusCultural capitalPublic relationsPolitical scienceSocial sciencePedagogyLaw

Abstract

fetched live from OpenAlex

This article examines a vocational education and training partnership occurring in the Canadian oil sands mining industry. The case study involves a corporate-sponsored pre-apprenticeship training programme designed to procure aboriginal labour in the province of Alberta. Interviews with members of key partner groups and stakeholders occurred during and after programme completion. Drawing from Pierre Bourdieu’s theory of the social field, capital reconversion strategies of partner groups are examined and critically evaluated in relation to the concept of ‘reputational risk management’ which I argue constitutes the underlying motive of the mine sponsor to procure racialised labour in order to maintain unfettered exploitation of resources whilst appeasing aboriginal resentment over land dispossession. Differential asset structures which partners bring to the partnership produce tensions that impact well-being and meaningful participation at the community level in the areas of education, training and employment.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.236
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.005
Scholarly communication0.0030.001
Open science0.0010.006
Research integrity0.0010.001
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 head0.016
GPT teacher head0.250
Teacher spread0.234 · 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
Published2015
Admission routes3
Has abstractyes

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