Bibliographic record
Abstract
At the turn of the century the American industrialist J.B. Duke set his sights on one of North America's greatest and most spectacular riversthe Saguenay.In Amassing Power David Massell chronicles thirty years of international intrigue as Duke manoeuvred to gain access to, develop, and sell the tremendous hydroelectric potential of a remote river in Quebec.The damming of the Saguenay brought industrialization on a grand scale to rural Quebec in the form of newsprint and aluminum manufacture.Tapping into rich and diverse sources in Canada, the United States, and Europe, Massell provides an interdisciplinary, cross-border study of American capital and Canadian resources.He shows us how ever-larger amounts of capital yielded increasingly massive and sophisticated applications of hydroelectric technology.Grand industrial plans, in turn, encroached upon provincial water rights and farmers' land, which drew the attention of the state.He examines the protracted power struggle between public and private interests -between American capitalists and the nascent bureaucracy of the province of Quebec -and describes the origins and evolution of the events that led to state control over hydraulic resources in the province.In doing so he provides vivid portraits of Duke and of Quebec politicians of the period and gives a dramatic account of the protracted battle of wits between Duke's chief engineer, William States Lee, and Quebec's chief of the Hydraulic Service, Arthur Amos.Amassing Power speaks to the integration of North American economies, vividly illustrating the process by which American capital drew Canada's resource-rich North into the economic orbit of the United States.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.857 | 0.805 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".