Bibliographic record
Abstract
T his the first first in a series of articles focusing on the Swing components that will be released as part of the Java Foundation Classes in JDK 1.2. I'll review the underlying infrastructure for the Swing components, in addition to some of the components Swing has to offer, in my next few installments. This month, after a brief history of the Java Foundation Classes, I'll discuss Swing's implementation of the Model/View/Controller (MVC) architecture. THE JAVA FOUNDATION CLASSES In addition to being a vast improvement over its predecessor, the 1.1 AWT lays the foundation for one of the most visible core Java APIs: Foundation Classes (JFC). The JFC consists of the 1.1 (and later) AWT, the Swing components, the 2D API, and the Accessibility API. HISTORY OF THE JFC Back in 1995, no one overestimated the impact that Java was about to have on the modern computing world. As a language originally designed for consumer electronic devices, Java was suddenly catapulted into the stratosphere as the language for developing Web software. Over the next couple of years, Java would mature quickly; not only the language but also core packages, such as the AWT. The original AWT was not designed to be a high powered UI toolkit—instead it was envisioned as providing support for developing simple user interfaces for simple applets. The original AWT was fitted with an inheritance-based event model that did not scale well.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.037 |
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".