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Record W3212577576 · doi:10.1016/j.exis.2021.101008

Schefferville revisited: The rise and fall (and rise again) of iron mining in Québec-Labrador

2021· article· en· W3212577576 on OpenAlexafffundabout
Thierry Rodon, Arn Keeling, J-S Boutet

Bibliographic record

VenueThe Extractive Industries and Society · 2021
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsMemorial University of NewfoundlandUniversité Laval
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHuman settlementIndigenousBoomSettlement (finance)PoliticsEconomyGeographyPolitical scienceEconomic geographyEconomicsArchaeologyEngineering

Abstract

fetched live from OpenAlex

The impact of “boom-bust” industrial cycles and mine closure on mining communities is a subject of long standing in research on extractive industries. Relatively few studies incorporate a historical, longitudinal approach to the economic and demographic changes associated with these cycles at a community and regional scale. This paper revisits a series of classic studies undertaken in the 1980s of industrial cycles in the Québec-Labrador mining region of Canada, and updates them by tracing some of the impacts of the rapid rise and fall of iron prices since the early 2000s. Drawing from field observations, community interactions, and socio-economic data on several regional mining settlements, it considers the social impacts of these increasingly rapid industrial cycles on northern mining communities, as well as Indigenous communities. Using the conceptual lens of staples theory and political economy, the paper explores the influence of past episodes of closure and dislocation on contemporary industrial cycles in the region. It also accounts for the shifting institutional and political contexts affecting recent mining cycles, including the role of the state, environmental issues, and Indigenous rights. The results reveal that continued reliance on mining keeps these remote communities tied to global trends in iron ore and steel production, meaning they will continue to be exposed to the stresses and strains of industrial cycles to come. However, these impacts are experienced differently across the region, based on intraregional differences in local demography, economy, and settlement history.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.279

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 studies0.0000.000
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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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 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

Citations9
Published2021
Admission routes3
Has abstractyes

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