Bibliographic record
Abstract
UML is used as a language for object-oriented software design, and as a language for conceptual modeling of applications domains. Given the differences between these purposes, UML’s origins in software engineering might limit its appropriateness for conceptual modeling. In this context, Evermann and Wand have proposed a set of well-defined ontological rules to constrain the construction of UML diagrams to reflect underlying ontological assumptions about the real world. The authors extend their work using a design research approach that examines these rules by studying the consequences of integrating them into a UML CASE tool. The paper demonstrates how design insights from incorporating theory-based modeling rules in a software artifact can be used to shed light on the rules themselves. In particular, the authors distinguish four categories of rules for implementation purposes, reflecting the relative importance of different rules and the degree of flexibility available in enforcing them. They propose distinct implementation strategies that correspond to these four rule categories and identify some redundant rules as well as some rules that cannot be implemented without changing the UML specification. The rules are implemented in an open-source UML CASE tool.
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 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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| 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".