Financiers in the forests on Vancouver Island, British Columbia: On fixes and colonial enclosures
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".