Bibliographic record
Abstract
Aspect-oriented mechanisms are characterized by their join point models. A join point model has three components: join points, which are elements of language semantics; "a means of identifying join points"; and "a means of affecting the behaviour at those join points." A pointcut-advice model is a dynamic join point model in which join points are points in program execution. Pointcuts select a set of join points, and advice affects the behaviour of the selected join points. In this model, join points are typically selected and advised independently of each other. That is, the relationships between join points are not taken into account in join point selection and advice. In practice, join points are often not independent. Instead, they form part of a higher-level operation that implements the intent of the developer ( e.g. managing a resource). There are natural situations in which join points should be selected only if they play a specific role in that operation. We propose a new join point model that takes join point interrelationships into account and allows the designation of more complex computations as join points. Based on the new model, we have designed an aspect-oriented construct called a transactional pointcut (transcut) . Transcuts select sets of interrelated join points and reify them into higher-level join points that can be advised. They share much of the machinery and intuition of pointcuts, and can be viewed as their natural extension. We have implemented a transcuts prototype as an extension to the AspectJ language and integrated it into the abc compiler. We present an example where a transcut is applied to implement recommended resource handling practices in the presence of exceptions within method boundaries.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".