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Record W4391260560 · doi:10.51327/mudy7900

Oneness as a Way to Business for Good

2024· article· en· W4391260560 on OpenAlexaff
Tom Culham

Bibliographic record

VenueJournal of Management Spirituality & Religion · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Hyper individualism accepted by business schools is bad theory resulting in management practices with negative consequences for students and society. Business is a social activity, and human consciousness includes intentions and values. All of these factors must be included in management theory for good people to do good. The brain's left and right hemisphere consciousness are very different. The left favours division and acquisitiveness to accomplish things while the right is oriented to Oneness and being in relationship. Management education over emphasizes left hemisphere consciousness and neglects the right which needs to be developed for balance. Daoist and Buddhist philosophies are aligned with right hemisphere consciousness and provide pedagogical insights that were implemented in a third-year business ethics class. Research indicates students experienced Oneness-related phenomena therefore, it is possible to design Oneness education. Business hyper individualism can be shifted to a broader view resulting in a deeper connection with others, creating a better world.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.016
Scholarly communication0.0070.005
Open science0.0000.006
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.265
Teacher spread0.248 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations5
Published2024
Admission routes1
Has abstractyes

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