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Record W4393316312 · doi:10.1080/0023656x.2024.2320229

Introduction

2024· article· en· W4393316312 on OpenAlex
Marion Fontaine, Steven High, Lauren Laframboise

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLabor History · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Economy and Marxism
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDeindustrializationPoliticsContext (archaeology)Political scienceSociologyEconomyEconomicsHistoryLaw

Abstract

fetched live from OpenAlex

Far from simply being a ‘bookend’ of the industrial age, deindustrialization is an integral part of capitalist development and thus has a long history. In the North American context, the early scholarship on deindustrialization emerged from the efforts to resist plant closures as they were happening. More recently, the field of deindustrialization studies has been reinvigorated by authors who have shifted the centre of gravity from the US Rust Belt to Europe and increasingly to other parts of the world. This introduction traces the emergence and recent transformations in the field of deindustrialization studies. It also introduces this themed issue on the politics of deindustrialization, which tells the stories of shuttered mines, mills, and factories within the wider restructuring of the international division of labour in the late twentieth century. The articles, written from a range of disciplinary perspectives, extend outward to how workers, their unions, state actors, and the general public responded to the challenge of deindustrialization. The authors are all affiliated with the ‘Deindustrialization and the Politics of Our Time’ (DePOT) research project (deindustrialization.org), which brings together many of the world’s leading deindustrialization scholars to put the field in transnational perspective. By extending the range of comparisons and scales of analysis, this special-themed issue invites us to broaden our understanding of deindustrialization, both thematically and methodologically.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.706
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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