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Record W1969010829 · doi:10.1080/08832323.2012.670671

The Role of Economics in Canadian Undergraduate Business Education

2013· article· en· W1969010829 on OpenAlexaffabout
Colin F. Mang, Natalya Brown

Bibliographic record

VenueJournal of Education for Business · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsNipissing University
Fundersnot available
KeywordsAccreditationCurriculumBusiness educationEconomics educationExecutive educationNew business developmentManagerial economicsBusiness economicsBusiness relationship managementBusiness analysisBusiness managementEconomicsHigher educationElectronic businessManagementBusiness modelBusinessEconomic growthBusiness administrationMicroeconomics

Abstract

fetched live from OpenAlex

The authors analyzed curricula from all 61 Canadian undergraduate business programs and found that business schools that contain economics departments, business schools with higher mathematics and statistics requirements, and business schools that have received Association to Advance Collegiate Schools of Business accreditation are more likely to require additional economics courses beyond the standard introductory level microeconomics and macroeconomics. Among those schools that require one or more additional economics courses, managerial economics is the preferred requirement. The authors also found that many specialized business programs, such as human resources, finance, and international management, include either required or optional economics courses beyond those already required in the school's general business degree program. Based on the findings, the authors make recommendations for business school curriculum development.

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)0.005
metaresearch head score (Gemma)0.024
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 score0.930
Threshold uncertainty score0.511

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0070.003
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.353
Teacher spread0.333 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

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