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Record W4401974015 · doi:10.1177/10323732241267906

The Canadian Pacific Railway's diversification strategies: A financial performance story, 1883–2020

2024· article· en· W4401974015 on OpenAlexaffabout
Gary Spraakman, Stephan Fafatas, Davood Askarany

Bibliographic record

VenueAccounting History · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsYork University
Fundersnot available
KeywordsDiversification (marketing strategy)BusinessFinanceMarketing

Abstract

fetched live from OpenAlex

The diversification and financial performance literature is ambiguous. To shed light on this relationship, quantitative and qualitative research was used to study the Canadian Pacific Railway (CPR) from 1883 to 2020. The CPR was selected because of its extensive archives and past range of diversification activities. Most of CPR's diversification investments were unrelated and unsuccessful. There is a statistically significant negative relationship between diversification and operating income/Tobin's Q. This negative relationship can become U-shaped or even positive by increasing operating income via reduced costs, improved conversion of inputs into outputs, and improved service to customers. The absence of diversification is not sufficient to significantly increase performance; satisfactory financial performance only comes from increasing operating income. These findings contribute to understanding the nuanced relationship between diversification and financial performance, highlighting the advantages of pureplay and the necessity for continuous financial performance improvement with related diversification and even pureplay.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.626

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0070.005
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.175
Teacher spread0.162 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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