Bibliographic record
Abstract
The Unified Modeling Language, or the UML (Booch, Jacobson & Rumbaugh, 1999), has rapidly emerged as a standard language and notation for object-oriented modeling in systems development, while the accompanying Unified Software Development Process (Jacobson, Booch & Rumbaugh, 1999) has been developed to provide methodological support for application of the UML in software development. The UML is a non-proprietary modeling language managed by the Object Management Group, a not-for-profit consortium, which also manages several related modeling specifications. The UML has evolved from its initial version, with UML 2.0 formally adopted by the OMG in June 2003. This article is based on the UML 1.5 specifications (OMG, 2003), as those for UML 2.0 have not been finalized. However, the role of use cases appears to be essentially unaffected by the changes proposed for UML 2.0.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".