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Record W2797264088 · doi:10.1111/anti.12395

Naturalising Finance, Financialising Natives: Indigeneity, Race, and “Responsible” Agricultural Investment in Canada

2018· article· en· W2797264088 on OpenAlexafffundabout
Melanie Sommerville

Bibliographic record

VenueAntipode · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaInternational Development Research Centre
KeywordsRedressIndigenousAgriculturePoliticsLand grabbingValuation (finance)Investment (military)FinanceEconomicsPolitical economyPolitical scienceEcologyLaw

Abstract

fetched live from OpenAlex

Abstract This article examines the racialised political ecologies inscribed by financial investments in a large‐scale corporate farm engaging Indigenous peoples in the Canadian prairies. Established in 2009, One Earth Farms ( OEF ) became one of Canada's largest farms by leasing First Nations’ farmland. I argue that OEF 's early success hinged on its promise of “naturalising finance” by engaging agriculture as a purportedly more real and stable financial vehicle relative to traditional assets. Simultaneously, OEF claimed to facilitate First Nations’ participation in agriculture by integrating their land and labour with financial flows—effectively “financialising natives”. I document the specific opportunities for capital accumulation and valuation mobilised by the project's claims to be providing reparative historical redress to First Nations through investor and corporate ecological and social “responsibility”. Reflecting on colonisation and racialisation processes, I demonstrate the ways that Indigenous histories and subjectivities are mobilised and monetised in contemporary political ecological projects.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.201
Teacher spread0.192 · 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 designObservational
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

Citations22
Published2018
Admission routes3
Has abstractyes

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