Bibliographic record
Abstract
A nyone who has ever tried to construct modular, object-oriented user interfaces using the AWT knows how hard it can be. The result can easily end up being difficult to debug, complex to understand and maintain, and certainly not reusable (except by cutting and pasting!). However, huge benefits can be obtained by separating out the user interface from the application code. This has been acknowledged for a long time and the Java Development Kit included the Observer class and the Observable interface to support this. However, with the addition of the delegation event model in the JDK 1.1, the potential for separating the view and control parts of the interface was provided. This allows the separation of the interface from the control elements (i.e., what to do when a user presses a button) and from the application code. Such a separation is often referred to as a model-view-controller architecture (or just as the MVC for short). The MVC originated in Smalltalk, but the concept has been used in many places. This article considers what the MVC is, why it is a good approach to GUI construction, and what features in Java support it. It then describes a GUI application which has been built using the MVC architecture. The source code for this application is provided as an appendix.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".