Naturalising Finance, Financialising Natives: Indigeneity, Race, and “Responsible” Agricultural Investment in Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".