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Record W2485860960 · doi:10.1177/0308518x16660352

Resilient Settler Colonialism: “Responsible Resource Development,” “Flow-Through” Financing, and the Risk Management of Indigenous Sovereignty in Canada

2016· article· en· W2485860960 on OpenAlexaffabout
Anna Stanley

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2016
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsYork University
Fundersnot available
KeywordsSovereigntyIndigenousCorporate governanceResource curseColonialismState (computer science)Context (archaeology)Political sciencePolitical economyCapital (architecture)Indigenous rightsPoliticsEconomicsLawFinanceGeographyEcology

Abstract

fetched live from OpenAlex

This essay interrogates aspects of the recent reconfiguration of Canadian environmental resource governance in relation to Indigenous sovereignty, rights, and struggles for self-determination in the context of mining and mineral exploration. I am especially interested in the targeting of expressions of Indigenous sovereignty as threats to the “resilience” of the national economy and attempts to “manage” Indigenous sovereignty and rights through mechanisms of resource governance. I focus primarily on a set of changes made to “flow-through share” financing arrangements that allow mining firms to raise capital on the basis of tax credits for expenditures incurred in relation to engaging Indigenous rights in the mineral exploration process. I suggest that flow-through financing is a method for “risk managing” exposure to the threat of Indigenous sovereignty in the interests of mining capital and the state that produces effects of crown sovereignty. In closing, the essay considers relationships between the neoliberalization of Canadian environmental governance and settler colonialism.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.894

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.012
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.155
Teacher spread0.150 · 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

Citations36
Published2016
Admission routes2
Has abstractyes

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