Bibliographic record
Abstract
F or those who are just joining us, welcome! We are exploring common and useful object-oriented analysis and design modeling activities that ultimately lead to the creation of a system implemented in Java. By definition, because analysis focuses on investigation of the problem space, the analysis models do not directly relate to Java. However, as we move on to design, we will explore more Java-related issues that impact the design of the architecture and software classes. When diagrams are used, we illustrate them in the Unified Modeling Language (UML) notation. However, this is not a column about the UML (which is “simply” a useful, standard diagramming notation—no small feat), rather it is a column about skills and heuristics in analysis and design, which is a more critical concern than notation. My usual disclaimer applies: modeling and diagramming should practically aid the development of “better” software—better in meeting the desires of the client or in being easier to change and extend. If it doesn't, question its value. In our last column on conceptual (or domain object) models (see “The Conceptual Model—What's the Object?,” Java Report , Vol. 3, No. 10) we focused on the fundamentals of this classic object-oriented analysis model: identifying concepts, attributes, and associations. It is not a picture of software components or classes; it is an analysis-oriented set of diagrams that depict abstractions of things of interest in the problem domain.
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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.028 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".