Using a Business Ontology for Structuring Artefacts
Bibliographic record
Abstract
This case story describes how the Business Ontology can be used to structure organizational artefacts. Northern Health was chosen for this case story because of the complexity and nature of their industry. Using a Business Ontology for structuring artefacts exemplifies the power of integrated and standardized artefacts in facilitating alignment, transformation and the management of a complex project portfolio involving multiple programs and projects through a structured way of thinking, way of working and way of modelling. This case story will discuss what artefacts are, what they consist of, their purpose, and how the Global University Alliance's Business Ontology was used effectively to develop a structured way of thinking, working and modelling. Furthermore, this paper presents the artefacts that provided the foundation for developing Northern Health-specific reference content and explains how they could be reused by various projects within the organization. It also includes the lessons learned on where these artefacts could be applied and the benefits of applying them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".