A Parasite Not a Cannibal? How the State and Capital Protect Accumulation Amid Devastation
Bibliographic record
Abstract
Abstract Nancy Fraser's recent book, Cannibal Capitalism , breathes new life into the eco‐Marxist concept of the ecological contradiction, arguing capitalism destroys its own ecological conditions of possibility like a serpent eating its own tail. Fraser's thesis appears to be playing out in British Columbia forests, where industry is closing mills and cutting jobs, decrying an increasingly limited “fibre basket”. But amid the ecosystem degradation industrial forestry has wrought over decades, including impacts to now‐endangered caribou, forestry firms and the state protect capital's ability to accumulate: firms move capital outside BC; the state replenishes trees, maintains “investability”, and attempts to avoid caribou extinction without constricting capital's access to nature. Capitalism thus appears more parasitic than cannibalistic. Taking a long view, BC forestry is, like capitalism broadly, durable despite being anti‐ecological, in part due to the state's powerful stabilising role.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".