Mass Destruction: The Men and Giant Mines That Wired America and Scarred the Planet. By Timothy J. LeCain. (New Brunswick: Rutgers University Press, 2009. xiv, 273 pp. $26.95, ISBN 978-0-8135-4529-5.)
Bibliographic record
Abstract
The title sounds sensationalistic, but Mass Destruction is a solid contribution to environmental history as well as to the history of metal mining in America. There has been a tendency among academics to view these two fields as mutually exclusive; at the annual conference of the Western History Association, for example, the breakfast meeting of the mining historians has typically been scheduled opposite that of the environmental historians, making it impossible for a member of one group to attend the breakfast of the other. But Timothy J. LeCain has nicely bridged any perceived gap between the two fields in Mass Destruction. Based solely on the book's title, I was prepared to read an antimining diatribe. In fact, what LeCain provides is an uncommonly perceptive and nuanced look at copper mining and its important contributions to the electrical revolution of the late nineteenth and early twentieth centuries. He organized his story around the life of Daniel Cowan Jackling (1869–1956), a Missouri-born and -trained metallurgist. Jackling revolutionized copper mining around the world during a time of rapidly growing demand by bringing the open-pit method and speedy production to the mining of low-grade ore at Utah's Bingham Canyon (1906) and doing so at a profit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".