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Record W2047012537 · doi:10.7202/030493ar

Organizational Culture and Radical Technological Change: The Railway Locomotive Industry During the Twentieth Century

2006· article· en· W2047012537 on OpenAlexvenueno aff
Albert Churella

Bibliographic record

VenueJournal of the Canadian Historical Association · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsHorsepowerDiesel locomotiveEngineeringSteam engineFlexibility (engineering)Competitor analysisSteam powerManufacturing engineeringIndustrial organizationBusinessManagementAutomotive engineeringMarketingMechanical engineeringEconomicsWaste management

Abstract

fetched live from OpenAlex

Beginning in the 1930s, North American railroads began replacing their steam locomotives with diesels at an ever-accelerating rate. Established steam locomotive producers, most notably the American Locomotive Company and the Baldwin Locomotive Works, proved incapable of dealing with this radical technological discontinuity. As successful steam locomotive manufacturers, these firms developed a corporate managerial culture that was not only linked closely to steam locomotive technology; it also embodied the fundamentals of small-batch custom manufacturing. More successful competitors, such as Electro-Motive (later a division of General Motors), developed a corporate culture amenable to both diesel locomotive technology and the standardized near-mass-production techniques that made diesel production efficient and profitable. Electro-Motive executives understood that railroad customers increasingly valued performance characteristics (flexibility, lower operating costs) best fulfilled by diesels, while steam locomotive producers continued to concentrate on the outdated characteristics (horsepower, low initial cost) of steam locomotive technology.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.995

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.164
Teacher spread0.156 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2006
Admission routes1
Has abstractyes

Explore more

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