Before the Conservation Fix: Ecological Displacement and the Making of Nature as Regulatory Subject
Bibliographic record
Abstract
Abstract Liberal states must reconcile extraction‐driven economic growth with environmental protection. While literature on environmental fixes documents how conservation measures can ease this tension, it has yet to fully explore the conditions, which normalise the transformation of overexploitation into an ecological, rather than economic, problem. Using the entanglement of British Columbia's wolf cull, resource industries and endangered caribou as a case study, I draw from conservation archives, economic data and theories of liberal environmental governance to show how balancing calls for both extraction and environmental protection allows for the state to engage in ‘ecological displacement’, where regulation is shifted from economy to ecology. Further, I argue that this displacement is dependent on pre‐existing domination of animal life. These findings suggest that understanding the proliferation of what geographers call conservation fixes requires engagement with conditions of the liberal state that make lethal ecological intervention more available than regulation of extractive interests.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.072 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".