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Record W2034627570 · doi:10.7202/1015921ar

Interpreting Personalized Industrial Heritage in the Mining Towns of Cumberland County, Nova Scotia: Landscape Examples from Springhill and River Hebert

2007· article· en· W2034627570 on OpenAlexfundvenueaboutno aff
Robert Summerby‐Murray

Bibliographic record

VenueUrban History Review · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNova scotiaDeindustrializationCollective memoryNarrativeAmbiguityCultural heritageIndustrial heritageInterpretation (philosophy)GeographyPublic historyCultural landscapeArchaeologySociologyCultural heritage managementPolitical scienceArtComputer scienceLaw

Abstract

fetched live from OpenAlex

As analysis of deindustrialization shifts from economic processes to community response, public landscapes become the locus for the struggle over memory. The interpretation of collective memory has been considered through oral histories, worker narratives, and public art. Missing from this analysis, however, are the personalized landscapes that also contribute to the understanding of industrial heritage in deindustrializing cities and small towns. This article considers a small number of artifacts and constructed objects that create personalized landscapes of industrial heritage in two mining towns in Cumberland County, Nova Scotia. Interpreting these landscapes highlights their ambiguity, the contributions they may make to processes of local cultural resistance, and their intensely personal motivation. The analysis questions the extent to which these landscapes reflect a wider coherent heritage discourse or function to reinforce local community, family, and place identity.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.733

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.003
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.156
GPT teacher head0.250
Teacher spread0.095 · 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 designQualitative
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

Citations18
Published2007
Admission routes3
Has abstractyes

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