Bibliographic record
Abstract
J avabeans are reusable software components that are assembled using a visual assembly tool to create Java applets or applications. The JavaBean standard provides a software component architecture for Java that promises to deliver rapid application development (RAD) by enhancing reuse and increasing the abstraction level of software development, enabling even non-programmers to develop Java applets and applications. JavaBeans derive power because one can configure and connect beans to achieve sophisticated functionality without knowing all of the internal details of beans themselves. In principle, a bean may be used by just knowing “what” it does without necessarily knowing “how” it works. For example, a loan calculator bean can calculate the principal and interest due on a loan. Generally, this is sufficient information to use the bean without having to know the details of the algorithm the bean uses. However, this requires some measure of trust on the part of the person using the bean. To draw again on the loan calculator bean example—if the configured loan calculator bean says we need to make a payment of $550 per month for the term of the loan, we need to trust that this result is correct without meticulously working through the beans implementation to ensure it calculates correctly. Clearly, without this trust the bean loses much of its power as a software component for reuse and RAD, and worse still, fuels the “not invented here” syndrome.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.025 |
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".