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Record W2139143122 · doi:10.1109/enabl.1994.330502

A cost ontology for enterprise modelling

2002· article· en· W2139143122 on OpenAlexaff
K. Donald Tham, Mark S. Fox, Michael Grüninger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsActivity-based costingOntologyComputer scienceService (business)Enterprise modellingRepresentation (politics)Knowledge managementEnterprise integrationEnterprise softwareBusinessAccountingMarketing

Abstract

fetched live from OpenAlex

There is an urgent need to formalize activity-based costing (ABC) for the purposes of implementation and usage in enterprises so that enterprises have access to the critical element of global success, viz. strategic management accounting. To make this possible, the authors present a core cost ontology and micro-theory of costing for enterprise modelling that spans the knowledge representation of activity, status of activity, time, causality, and resources. This ensures that ABC may be generically deployed in any enterprise to achieve effective activity-based cost management, irrespective of the enterprise belonging to the manufacturing or service sectors.>

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)0.003
metaresearch head score (Gemma)0.005
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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0030.005
Science and technology studies0.0020.002
Scholarly communication0.0060.009
Open science0.0020.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.002

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 head0.096
GPT teacher head0.253
Teacher spread0.157 · 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

Citations44
Published2002
Admission routes1
Has abstractyes

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