Bibliographic record
Abstract
The unified modeling language (UML) emerged in the mid-1990s through the combination of previously competing object-oriented systems analysis and design methods, including Booch (1994), Jacobson, Christerson, Jonsson, and Overgaard (1992), Rumbaugh, Blaha, Premerlani, Eddy, and Lorensen (1991) and others. Control over its formal evolution was placed in the hands of the object management group (www.omg.org), which recently oversaw a major revision to UML 2.0 (OMG, 2005). The UML 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 applying the UML in software development. Use cases play an important role in the unified process, which is frequently described as “use case driven” (e.g., Booch et al., 1999, p. 33). The term “use case” was introduced by Jacobson (1987) to refer to a text document that outlines “a complete course of events in the system, seen from a user’s perspective” (Jacobson et al., 1992, p. 157). The concept resembles others being introduced around the same time. Rumbaugh et al. (1991), Wirfs-Brock, Wilkerson, and Wiener (1990), and Rubin and Goldberg (1992) use the terms “scenario” or “script” in a similar way. While use cases were initially proposed for use in object-oriented analysis and are now part of the UML, they are not inherently object-oriented and can be used with other methodologies.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".