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Record W2894565937 · doi:10.1111/joac.12294

Financiers in the forests on Vancouver Island, British Columbia: On fixes and colonial enclosures

2018· article· en· W2894565937 on OpenAlexaffabout
Michael Ekers

Bibliographic record

VenueJournal of Agrarian Change · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsColonialismContext (archaeology)IndigenousNegotiationCapital (architecture)State (computer science)PoliticsDirtLand tenureDeregulationEconomicsEconomyFinancePolitical economyMarket economyPolitical scienceGeographyLaw

Abstract

fetched live from OpenAlex

Abstract Starting in the mid 2000s, a financial asset management company and institutional investors began to invest in timberlands in British Columbia, Canada's most western province. In a period of political economic crisis, investors looked to real assets—“dirt and trees” in the words of one research participant—as a means of accumulating capital through securing access to huge parcels of the most productive and valuable forestland in North America. This article analyses these investments as a socioecological fix for finance capital suggesting that investments in land represent a means for capital and the state to negotiate moments of crisis. The article complicates existing accounts of fixes by demonstrating how the survival of capital in a settler context is fully dependent on an ongoing settler‐colonial project of separating Indigenous people from their land base. The article focuses on the explicitly “private” nature of the land under examination and how this is central to the strategies of investors, the state's deregulation of forest policies, and the marginalization of First Nations' claims to land. The article demonstrates that in settler contexts, discussions of fixes need to be much more attentive to the historic and enduring colonial threads woven through investments in land.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.372

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 head0.021
GPT teacher head0.209
Teacher spread0.188 · 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

Citations20
Published2018
Admission routes2
Has abstractyes

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