The ‘Green War’: Geopolitical Metabolism and Green Extractivisms
Bibliographic record
Abstract
Through ‘green transitions’ and ‘climate action’, geopolitical actors seem to be vying for dominance based on justifications that articulate geopolitical, economic, and ecological concerns. This ‘Green War’ geopolitical framing advances and consolidates imperial power through policies seeking to promote and secure ‘low carbon technology’ and ‘critical minerals’ supply chains, intensifying what we see as a novel ‘geopolitical metabolism’. Whereas geopolitical metabolisms used to notably focus on controlling the flow of fossil fuels, there is a growing emphasis on the ‘green extractivisms’ that underpin ‘green transition’ initiatives. This Green War framing and accompanying geopolitical metabolism enable states and corporations to advance and consolidate imperial power by capturing and perverting a much-needed geo-politics, or ‘politics of the Earth’. As such, we challenge the Green War geopolitical framing as part of the crucial effort to articulate an alternative geo-politics that promotes peace while reducing and redistributing energetic and material flows.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".