MétaCan
Menu
Back to cohort
Record W3092107427 · doi:10.47670/wuwijar201712nss

Family businesses and management information systems (MIS):Seven wise steps to become more electronically intelligent

2017· article· en· W3092107427 on OpenAlexaff
Nima S. Salami

Bibliographic record

VenueWestcliff International Journal of Applied Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsWycliffe College
Fundersnot available
KeywordsDreamBrickBusinessProcess managementBrick and mortarMortarManagementMarketingOperations managementComputer scienceEngineeringEconomicsCivil engineeringWorld Wide WebHistoryThe InternetPsychology

Abstract

fetched live from OpenAlex

Turning a brick-and-mortar family business into an E-business, would greatly pay off, and bring every manager’s dream come true, since it has the potential of creating a sustainable growth (Baltzan & Phillips, 2015). This paper will introduce the step by step guidelines for managers.

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)0.016
metaresearch head score (Gemma)0.021
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: none
Teacher disagreement score0.020
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0090.015
Scholarly communication0.0200.025
Open science0.0020.008
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0060.004

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 head0.090
GPT teacher head0.378
Teacher spread0.288 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueWestcliff International Journal of Applied ResearchSame topicBig Data and Business IntelligenceFrench-language works237,207