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Record W1595555216 · doi:10.7202/1025708ar

Wistful thinking: Making Inuit labour and the Nanisivik mine near Ikpiarjuk (Arctic Bay), northern Baffin Island

2014· article· en· W1595555216 on OpenAlexaffvenueabout
Frank Tester, Drummond E. J. Lambert, Tee Wern Lim

Bibliographic record

VenueÉtudes/Inuit/Studies · 2014
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsArcticSettlement (finance)Human settlementColonialismGeographyWork (physics)BayEthnologyThe arcticArchaeologyHistoryOceanographyGeologyEngineeringBusiness

Abstract

fetched live from OpenAlex

This article interrogates discourse about Inuit in relation to employment issues and the Inuit response to “what was good for them” at the intersection of colonial and postcolonial thought in the 1970s. Attempts to integrate Inuit with a modern industrial economy occurred after Inuit had moved, or been moved, from land-based hunting and trapping camps to new settlements developing in the eastern Arctic. We examine the planning stage (1970-1976) of the Nanisivik mine at Strathcona Sound on the northern tip of Baffin Island that operated from 1976 until 2002. Building on the work of James O’Connor in the early 1970s and concepts of legitimisation and accumulation functions of the State, and using the archival records of the Department of Indian Affairs and Northern Development and the Department of Energy, Mines, and Resources, we explore the extent to which Inuit were constructed as “labour in need of employment.” In examining debate between officials of these departments, we seek to find out to what extent other needs went unmet, based on experience with the Rankin Inlet Nickel Mine (1957-1962). Inuit resistance to this definition and the relationship between Inuit as hunters and Inuit as wage earners are explored with reference to contemporary mining development in Nunavut.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0080.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.036
GPT teacher head0.351
Teacher spread0.315 · 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.

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

Citations11
Published2014
Admission routes3
Has abstractyes

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