The dataflow pointcut
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Some security concerns are sensitive to flow of information in a program execution. The dataflow pointcut has been proposed by Masuhara and Kawauchi in order to easily implement such security concerns in aspect-oriented programming (AOP) languages. The pointcut identifies join points based on the origins of values. This paper presents a formal framework for this pointcut based on the λ_calculus. Dataflow tags are propagated statically to track data dependencies between expressions. We introduce a static semantics for tag propagation and prove that it is consistent with respect to the dynamic semantics of the propagation. We instrument the static effect-based type system to propagate tags, match and inject advices. This static approach can be used to minimize the cost of dataflow pointcuts by reducing the runtime overhead since much of the dataflow information would be available statically and at the same time it can be used for verification. The proposed semantics for advice weaving is in the spirit of AspectJ where advices are injected before, after, or around the join points that are matched by their respective pointcuts. Inspired from the formal framework, the AspectJ compiler ajc is extended with the dataflow pointcut that tracks data dependencies inside methods.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it