Fixing extraction through conservation: On crises, fixes and the production of shared value and threat
Bibliographic record
Abstract
We are currently witnessing a global trend of intensifying and deepening relationships between extractive companies and biodiversity conservation organisations that warrants closer scrutiny. Although existing literature has established that these two sectors often share the same space and rely on similar logics, it is increasingly common to find biodiversity conservation being carried out through partnerships between extractive and conservation actors. In this article, we explore what this cooperation achieves for both sectors. Using illustrative examples of extractive-conservation collaboration across sub-Saharan Africa, we argue that new entanglements between extractive and conservation actors are motivated by multiple purposes. First, partnering with conservation actors serves as a spatial and socio-ecological fix for extractive companies in response to multiple crises that threaten the sector's productivity. Second, new forms of collaboration between extractive and conservation actors create pathways for both sectors to produce new value from nature. For the extractive sector, creating new value from nature works as a further fix to capitalist crises whereas, for the conservation sector, producing value through nature amounts to new opportunities for capital accumulation. Importantly, working together to produce shared value from nature within and beyond extractive concessions secures both sectors' control over the means of production. Theoretically, our analysis links literature on value in capitalist nature with that on spatial and socio-ecological fixes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.049 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".