Architectures of Extraction: Labor and Industrial Ruination in Highland Bolivia
Bibliographic record
Abstract
What happens when the architectures of extraction, once intimately constituted by capitalist and racial forms of exclusion, begin to rot? Focusing on a Bolivian tin mine, this paper examines the social effects of deteriorating “architectures of extraction,” a category that includes both aboveground infrastructures and belowground networks of tunnels and scaffolding. First, I argue that when Llallagua’s extractive architecture was built, it helped shore up a connection between tin mining, working-class identity, and revolutionary nationalism – which in turn became bound up with mestizaje, an ideology of racial and cultural whitening. Second, I argue that the ruination of this extractive architecture has had ambivalent implications for the racial politics of contemporary small-scale mining operations that continue to operate inside the mountain. On the one hand, the rotting structures reinforce regional racial hierarchies in a variety of ways, but on the other hand, the slow degradation of the physical structures has made the corresponding social structures more porous, if not fully permeable, to members of regional Indigenous ayllus. While access to the financial benefits of mining is far from unambiguously liberatory, this paper nevertheless suggests that the political possibilities contained within the ruins of extractive architectures are not uniformly adverse.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".