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Record W4378193481 · doi:10.52975/llt.2023v91.009

Importing the Clairtone Sound

2023· article· en· W4378193481 on OpenAlexaffvenueabout
Lachlan MacKinnon

Bibliographic record

VenueLabour / Le Travail · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsCape Breton University
Fundersnot available
KeywordsDeindustrializationNova scotiaEconomyCorporationState (computer science)IndustrialisationPolitical scienceEconomicsMarket economyGeography

Abstract

fetched live from OpenAlex

Following the industrial crisis of the 1920s and the Great Depression in the 1930s, consecutive provincial governments in Nova Scotia turned their efforts toward state-led economic development. After the election of Robert Stanfield and the Tories in 1956, a wholesale industrial planning model was unveiled. Indeed, Stanfieldian economic policy in Nova Scotia was predicated upon the belief that direct state-led interventionism was necessary to offset regional inequity. State corporate entities, such as Industrial Estates Limited, and renewed interest in a state-driven industrial relations paradigm were central in the province’s efforts to revitalize its flagging economy and offset predicted decline in the Cape Breton coal and steel industries. This article examines the fate of the Clairtone Sound Corporation, one of Nova Scotia’s “new industries” that emerged out of these state-led development efforts. A case study of this Stellarton-based firm reveals how structural processes of deindustrialization produced crisis even within sectors that were completely distinct from the province’s cornerstone industries of coal and steel. This case includes a reflection on the class composition of the modernist state in Nova Scotia and represents a convergence of the historiographical focus on state-led industrial development in the Maritimes and recent literature found within deindustrialization studies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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 teacher head, not a consensus.

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

Citations1
Published2023
Admission routes3
Has abstractyes

Explore more

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