MétaCan
Menu
Back to cohort
Record W2313055233 · doi:10.1093/jahist/97.2.534

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.)

2010· article· en· W2313055233 on OpenAlexaboutno aff
Carlos A. Schwantes

Bibliographic record

VenueJournal of American History · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryCanyonEconomic historyCopper mineArt historyGeographyCartographyCopperChemistry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.010
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.159
Teacher spread0.154 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of American HistorySame topicAmerican Environmental and Regional HistoryFrench-language works237,207