Bibliographic record
Abstract
In the web services environment software development can involve writing both object-oriented programs and XML transformations. This can be seen in the popular Web Services architecture. In this architecture, crosscutting concerns are often manifest as transformations on XML messages; encrypting messages, adapting between schemas types or adding extra-functional elements such as transaction contexts can be seen as prime examples. Some existing middleware platforms provide support for Handlers where crosscutting message transformation concerns can be addressed. Although handlers localize some concerns, they do not support the sound software-engineering principle of "programming to an interface". This prevents a clean mapping from design to implementation and inhibits useful static checking which could take advantage of a well specified interface. To address this and similar design challenges, we have developed Doxpects, which solve many problems with the existing handler approach to implementing these new crosscutting concerns. We describe an AOP abstraction called the content-based pointcut which integrates support for XML transformation to enable implementation of crosscutting concerns with good modularity properties. We present examples based on XML encryption and service interoperability implemented on top of the Apache Axis Web Services middleware.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".