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Record W575724559 · doi:10.3138/9781442620902

Industrial Sunset: The Making of North America's Rust Belt, 1969-1984

2003· book· en· W575724559 on OpenAlexaboutno aff
Steven High

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2003
Typebook
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsDeindustrializationPoliticsMillFactory (object-oriented programming)SolidarityHistoryGeographyEconomic historyEconomyPolitical scienceArchaeologyLaw

Abstract

fetched live from OpenAlex

Plant shutdowns in Canada and the United States from 1969 to 1984 led to an ongoing and ravaging industrial decline of the Great Lakes Region. Industrial Sunset offers a comparative regional analysis of the economic and cultural devastation caused by the shutdowns, and provides an insightful examination of how mill and factory workers on both sides of the border made sense of their own displacement. The history of deindustrialization rendered in cultural terms reveals the importance of community and national identifications in how North Americans responded to the problem.Based on the plant shutdown stories told by over 130 industrial workers, and drawing on extensive archival and published sources, and songs and poetry from the time period covered, Steve High explores the central issues in the history and contemporary politics of plant closings. In so doing, this study poses new questions about group identification and solidarity in the face of often dramatic industrial transformation

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)0.000
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.128
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0140.005
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.221
Teacher spread0.193 · 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
GenreOther

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

Citations88
Published2003
Admission routes1
Has abstractyes

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