MétaCan
Menu
Back to cohort

The Unreliable Nation

2017· book· en· W4234743817 on OpenAlexaboutno aff
Edward Jones‐Imhotep

Bibliographic record

VenueThe MIT Press eBooks · 2017
Typebook
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsNatural (archaeology)Political scienceIdentity (music)Political economyHistoryEnvironmental ethicsGeographyMedia studiesSociologyLawAestheticsArchaeology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.017
Scholarly communication0.0100.007
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.041
GPT teacher head0.266
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations24
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe MIT Press eBooksSame topicPolar Research and EcologyFrench-language works237,207