THE UNIVERSITY OF CALGARY Improving the Modularity of Context-Sensitive Concerns through the Use of Declarative Event Patterns
Bibliographic record
Abstract
Software modules communicate with one another to form coherent software systems. In order to facilitate this interaction, knowledge of the communication protocol is split up and embedded in each participating module, making local reasoning about the protocol difficult. Modules themselves become collectively responsible for seeing that the appropriate sequence of messages transpires. With protocols scattered in this way, and tangled amongst the details that intrinsically belong inside modules, the traceability, comprehensibility, and maintainability of the concern they represent, and of the system as a whole, tend to suffer. Declarative event patterns (DEPs) are a means to implement communication pro-tocols between modules in a localized manner. DEPs describe sequences of events in the execution of a system and include the ability to recognize properly nested structures. They allow a developer to describe a protocol at a high level, without the need to express extraneous details. A developer can indicate that specific actions be taken when a given pattern occurs. Protocol patterns are automatically translated
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.020 |
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".