Underground Leviathan: Corporate Sovereignty and Mining in the Americas. By Israel G. Solares
Bibliographic record
Abstract
Mining historians face a fundamental challenge: how to appropriately scale studies of an industry that is at once intensely local and extensively global. Most choose the former over the latter, crafting accounts of small mining towns, local labor disputes, and geographically discrete environmental impacts. Such histories risk losing sight of the fact that “local” mining places are almost invariably created by flows of capital, labor, technocratic knowledge, and heavy equipment from distant places, often coordinated by a corporation that operates mines all over the world. Israel Solares’s new book, Underground Leviathan, openly challenges the overly local frame of many mining histories. His focus is the broad reach of multinational mining corporations in the early twentieth century, in particular the United States Company, founded in Maine but with operations throughout the Americas. Solares argues that the modern multinational represented a projection of sovereign power, a Leviathan-like political entity that was not a mere outgrowth of state or colonial ambitions, but a dominant actor in pursuit of its own objectives. But rather than focus solely on the top-down politics of head office, shareholders, or the board room, Solares also claims that the “Leviathan” of the modern corporation is made up of the social interactions of multiple actors: workers, engineers, and middle managers, but also local farmers and other residents who had to contend with adjacent mining operations.
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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 | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".