Bibliographic record
Abstract
Natures and technologies have long been central to the making of modern nations. Only recently, however, have scholars seen nations as sites where the very understandings of the “natural” and the “technological” were articulated, contested, and remade in the interests of the nation. This book examines the role of technological failure in crafting both national identities and the distinctive natures that support them. Focusing on the mid-twentieth-century attempt to extend reliable radio communications to the Canadian North, it explores how a group of Canadian defense scientists sought to visualize, map, and catalog the connections between a distinctive natural order of ionospheric storms, auroral displays, and magnetic disturbances on one side, and the particularly severe communication failures that cut the North off from the rest of the nation on the other. Through that project and its related efforts, they gradually transformed machine failures in hostile environments, from the Arctic to outer space, into a defining characteristic of Canadian identity at a time of national redefinition. Tracking those efforts through continental defense strategies, engineering practices, clandestine maps, and material cultures, the book argues that the real and potential failures of machines came to define the nation, its Northern nature, its cultural anxieties, and its geo-political vulnerabilities during the Cold War. More broadly, it argues for technological failures as key sites for linking historical technologies and historical natures, and for writing the histories of “other” nations during the Cold War.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".