Bibliographic record
Abstract
With the recent advent of dynamically extensible software systems, in which software extensions may be dynamically loaded into the address space of a core application to augment its capabilities, there is a growing interest in protection mechanisms that can isolate untrusted software components from a host application. Existing language-based environments such as the JVM and the CLI achieves software isolation by an interposition mechanism known as stack inspection. Expressive as it is, stack inspection is known to lack declarative characterization and is brittle in the face of evolving software configurations. A run-time module system, ISOMOD, is proposed for the Java platform to facilitate software isolation. A core application may create namespaces dynamically and impose arbitrary name visibility policies to control whether a name is visible, to whom it is visible, and in what way it can be accessed. Because ISOMOD exercises name visibility control at load time, loaded code runs at full speed. Furthermore, because ISOMOD access control policies are maintained separately, they evolve independently from core application code. In addition, the ISOMOD policy language provides a declarative means for expressing a very general form of visibility constraints. Not only can the ISOMOD policy language simulate a sizable subset of permissions in the Java 2 security architecture, it does so with policies that are robust to changes in software configurations. The ISOMOD policy language is also expressive enough to completely encode a capability type system known as Discretionary Capability Confinement. In spite of its expressiveness, the ISOMOD policy language admits an efficient implementation strategy. In short, ISOMOD avoids the technical difficulties of interposition by trading off an acceptable level of expressiveness. Name visibility control in the style of ISOMOD is therefore a lightweight alternative to interposition.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".