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Record W4378193656 · doi:10.52975/llt.2023v91.0011

Superstack Nostalgia

2023· article· en· W4378193656 on OpenAlexaffvenueabout
Adam D.K. King

Bibliographic record

VenueLabour / Le Travail · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsYork University
Fundersnot available
KeywordsDeindustrializationTourismIndustrial heritageRestructuringHeritage tourismNarrativePolitical scienceEconomySociologyCultural heritageTourism geographyCultural heritage managementLawEconomicsArt

Abstract

fetched live from OpenAlex

The growth of industrial tourism and heritage has both fascinated and frustrated scholars of deindustrialization. Frequently, workers and class conflict are obscured in or expunged from the official narratives of industrial heritage. This article makes an original contribution to research on deindustrialization and industrial heritage through fieldwork in Sudbury, Ontario – a region that has seen a decades-long process of industrial restructuring. The article draws on 26 qualitative interviews with current and retired nickel miners and analyzes workers’ reflections on local mining history. It examines how workers understand the foreign takeover of the mines, job loss, and the transformation of Sudbury’s regional economy away from blue-collar industrial employment. The article then explores the growth of regional tourism based around the mining sector, looking particularly at Dynamic Earth, an attraction that teaches visitors about the history of nickel mining through guided tours of a closed mine. On the one hand, workers critique what they see as an obfuscation of class conflict in industrial heritage, while on the other hand, they experience these sites as confirmation of the historic contributions nickel miners have made to Sudbury and the surrounding region.

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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.998

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)0.0050.002

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 head0.076
GPT teacher head0.231
Teacher spread0.155 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2023
Admission routes3
Has abstractyes

Explore more

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