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
To help analyze unexpected behaviour, programming language environments and tools are beginning to support high-fidelity recordings of program executions. Such recordings are typically low-level and difficult to work with directly. Debugging and analyzing these recordings is easier and more powerful if it is possible to simulate executing additional code in the past context of the recording. In prior work we proposed retroactive weaving, the process of evaluating aspects as if they were present during a past execution. This concept is intended as a general framework for introducing additional code and defining the semantics of executing it post-hoc. In this paper we express retroactive weaving as a transformation on aspect-oriented programming languages and their semantics. We demonstrate this transformation by applying it to a simple core aspect-oriented language, and through a definitional interpreter illustrate its interactions with first-class function values, mutable state, and external input and output. In particular a key concern of retroactive weavers is maintaining soundness: behaving consistently with the context of the past execution, and failing if missing information makes this impossible. Retroactive weavers may need to include extra isolation or runtime checks to meet this requirement.
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 | 0.001 |
| 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 | 0.000 | 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