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Record W2013099367 · doi:10.4018/jssmet.2012010101

Information-Driven Framework for Collaborative Business Service Modelling

2012· article· en· W2013099367 on OpenAlexafffund
Thang Le Dinh, Thanh Thoa Pham Thi

Bibliographic record

VenueInternational Journal of Service Science Management Engineering and Technology · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersCanada Research Chairs
KeywordsService (business)Knowledge managementComputer scienceConceptual frameworkService designContext (archaeology)Service delivery frameworkProcess managementCollaborative networkCompetitive advantageBusiness

Abstract

fetched live from OpenAlex

In the context of a network of enterprises, the competitive advantage of each enterprise depends greatly on the ability to use network architectures to collaborate efficiently in business services. The paper introduces a conceptual framework, called the CBSM (Collaborative Business Service Modelling) framework, which provides an intellectual foundation for understanding thoroughly and modelling effectively collaborative business services. The paper begins by presenting the necessity for and principles of the conceptual framework. Then it presents the architecture of the CBSM framework that consists of three different levels: the service level for service operation, the service system level for service creation, and the service value creation network level for service proposal. The paper continues with a discussion and review of the relevant literature, followed by the conclusion and suggestions for further research.

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)0.004
metaresearch head score (Gemma)0.006
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: Methods · Consensus signal: Methods
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0030.004
Science and technology studies0.0020.003
Scholarly communication0.0070.005
Open science0.0060.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0070.003

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 head0.232
Teacher spread0.219 · 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
GenreMethods

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

Citations17
Published2012
Admission routes2
Has abstractyes

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