Claims, Debt and Equity in REA With FIBO Extensions
Bibliographic record
Abstract
This paper develops a method to identify and formally model how the REA concept of Claims fits into our understanding and extension of the core REA concepts in work derived from early FIBO (Financial Industry Business Ontology) and now characterized as the Semantic Shed’s Business Core Concept Ontology (BCO). In this exploration we determine that the notion of Claim relates to two important concepts, those of some imbalance between commitments and of the perspective on such imbalances. This paper sets out the basic concepts of REA and its later extensions by the FIBO team and the Semantic Shed community, in set theoretic concept intensions. The paper goes on to define the key concepts of imbalances and of perspectives on commitments and on imbalances. We then consider the definition of ‘Claim’ in REA as a terminological challenge. Some conclusions are given about where Claim fits in with the ontological terms given here and how Equity and Debt are framed with reference to these.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".