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Record W4312951419 · doi:10.57198/2583-4932.1094

Challenges in Management Education

2022· article· en· W4312951419 on OpenAlexaboutno aff
Kuldeep Kumar

Bibliographic record

VenueManagement Dynamics · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessMarketing

Abstract

fetched live from OpenAlex

With the advancement and exponential availability of the internet, mobile phones and smart phones, there is also consequent significant development and innovative advances in the multifaceted areas of management and commerce. For example, traditional commerce has shifted to electronic commerce (E-commerce) and mobile commerce (M-commerce), traditional marketing is shifting towards digital marketing or internet marketing, including Co-opetition (Cooperation and Competition), and, accordingly, there is more emphasis on psychological and service marketing. Globalization and its development are playing a major role in various management activities and trade. Fraud, bribery and corruption are increasing and there is more emphasis on Forensic Accounting to prevent and detect these frauds. Smart phones may be turned into virtual cash registers and businesses can track sales, inventories and other data on smart phones and tablets. This free app which has attracted millions of business in the US, Canada and Japan turns a tablet or smart-phone into a sales register that can be used to process payments while tracking data on sales, handle appointments and generate information for tax. Big data and data analytics are playing a crucial role in the finance arenas and also in market research. There are major changes in corporate governance and even small and medium enterprises and family business, who each are adopting these changes. The concept of corporate social responsibility and ethics is gaining momentum and consequently there is more emphasis in businesses on business ethics and corporate social responsibility as viable incentives to improve reputation and profits. Social media will play a crucial role in various management activities including global strategies and entrepreneurship.

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)0.022
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.039
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.013
Scholarly communication0.0170.016
Open science0.0030.014
Research integrity0.0110.020
Insufficient payload (model declined to judge)0.0390.007

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 head0.230
Teacher spread0.211 · 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
GenreCommentary

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
Published2022
Admission routes1
Has abstractyes

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