Remoteness and subjectivity in gas extraction: Indigenous agency and the roadless design
Bibliographic record
Abstract
• We highlight the link between constructing otherness, remoteness, and manufacturing consent. • We show how roadless gas extraction narratives are making Western environmental values more permeable. • We illustrate how local communities engage with and respond to extractivism’s environmental illusions. • We reveal how Indigenous people resist colonial extractivism and environmentalism with a nuanced agenda. Past research has confirmed how ‘green’ extractive projects can reproduce exclusion and displacement overall, but constructions of otherness and remoteness that emerge in such green illusions of extractivism and their resistance remains little understood. Peru’s Camisea liquid natural gas (LNG) extraction in the Peruvian Amazon has been framed as an environmentally friendly flagship project because of its enclave or roadless design that enables a smaller environmental footprint. Drawing on a political ecology analysis of subject formation and co-production of remoteness, this paper analyzes the agendas and effects of constructed “remoteness” in its resource extraction as a strategy to design, legitimize, and enforce territorial control. This analytical lens moves away from strict binaries of the powerful and the powerless towards a continuum of power in the resistance of extraction. We found that the notion of ‘remoteness’ is a central rhetorical strategy that paradoxically enables and limits corporate expansion, neoliberal agendas and Indigenous tactics to negotiate access to benefits. This study contributes to and works toward a more diversified power knowledge base on the ways in which environmental claims in extractivism are assessed.
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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 | 0.005 |
| 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 | 0.057 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".