REA model, its development and integration as an enterprise ontology framework
Bibliographic record
Abstract
Research Question: REA enterprise ontology framework, what is it good for? Motivation: The historical approach to accounting and management information system design was based on conventions expected by the end-users: debits and credits, accounting cycles, general ledger and journals, bank reconciliations, budgeting function, and select management reports. This approach resulted in gross inefficiencies, data-duplication, and inconsistencies, difficulty with system update, modification, porting, and restoration. An alternative system design theory has been in development since 1982, an approach that is easy to understand, formulate, document, and implement; an approach that applies a basic semantic model of structuring all information flow into a widely applicable enterprise ontology framework that facilitates economic activities and strategic planning for the whole enterprise. Yet, until now, this approach is insufficiently known and seldom utilized. Idea: Our purpose is to provide a comprehensive theory guide for anyone desiring to be acquainted with the REA. Data: We review 55 publications comprising dominant Resource-Events-Agents (REA) theory research. Tools: Methodologically, we obtain, classify, define, and discuss the content of major research streams within REA domain. Contribution: The paper's contribution is in structured and comprehensive review enabling a novice to REA reader time-efficient acquaintance with the intricacies and benefits of the ontology, and information system researchers with wide-ranging theory review in this domain. We conclude with a discussion of contentions and challenges surrounding REA theory and its future developmental directions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".