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Record W2621500415 · doi:10.1177/0263775817713209

How does a settler state secure the circuitry of capital?

2017· article· en· W2621500415 on OpenAlexaffabout
Shiri Pasternak, Tia Dafnos

Bibliographic record

VenueEnvironment and Planning D Society and Space · 2017
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of New BrunswickToronto Metropolitan University
Fundersnot available
KeywordsIndigenousJurisdictionSovereigntyCapital (architecture)BusinessPoliticsState (computer science)Political economyPolitical scienceLawEconomicsGeography

Abstract

fetched live from OpenAlex

Indigenous peoples interrupt commodity flows by asserting jurisdiction and sovereignty over their lands and resources in places that form choke points to the circulation of capital. In today’s economy, the state has begun to redefine its “resilience” in terms of its relative success in the protection and expansion of critical infrastructure. We find that there has been a political re-organization of governing authority over Indigenous peoples in Canada as a result, which is driven by greater integration of the private sector as national security “partners.” The securitization of “critical infrastructure”—essentially, supply chains of capital, such as private pipelines and public transport routes—has become the priority in mitigating the potential threat of Indigenous jurisdiction. New political and socio-temporal imperatives have led to shifts in risk evaluation, management, and mitigation practices of state administration, in cooperation with the private sector, to neutralize Indigenous disruption to supply chain infrastructure. In this paper, we examine two forms of risk mitigation: first, the configuration of Indigenous jurisdiction as a “legal risk” by the Department of Indigenous and Northern Affairs Canada; and second, the configuration of Indigenous jurisdiction as a source of potential “emergency.” Built on the literal ground of historical patterns of land grabs and migration, logistical space configures new networks of infrastructure into circuitries of production that cast into vivid relief the imperfections of settler sovereignty and the vital systems of Indigenous law.

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)0.002
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.360
Threshold uncertainty score0.715

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.016
Scholarly communication0.0080.005
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.007
GPT teacher head0.173
Teacher spread0.166 · 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

Citations156
Published2017
Admission routes2
Has abstractyes

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