Bibliographic record
Abstract
T he J ava R eport published its first issue in March 1996. This was a real accomplishment when you consider that it was only on May 23, 1995, that John Gage, director of the Science Office for Sun Microsystems, announced Java to the world at SunWorld. Later in 1995, Sun released the initial Java Development Kit (JDK) and a Java enabled Web Browser called HotJava. The rest is history, as Java is now firmly entrenched in the computing industry. Many of us who saw demonstrations of Java in 1995 knew that it was something new, something different, and something not to be ignored. Those behind the Java Report knew this too, and we have been reporting on Java ever since. In his first editorial for Java Report , the original Editor-In-Chief, David Fisco, wrote: The Java community is becoming broader every day, encompassing CIOs, information technologists, market professionals, programmers, multimedia designers, educators, managers, and even hobbyists. … However, many CIOs, developers, and even software experts are having a hard time getting a handle on Java. Some have said that it's just a neat way to run animations on the Web, others note that Java enables Web-based electronic transaction, and still others tout Java as the Holy Grail that will bring about the $500 PC and change the world of computing as we know it.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.373 | 0.261 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".