Bibliographic record
Abstract
One of the accepted techniques for developing and maintaining feature-rich applications is to treat each feature as a separate concern. However, most features are not separate concerns because they override and extend the same basic service. That is, “independent” features are coupled to one another through the system's basic service. As a result, seemingly unrelated features subtly interfere with each other when trying to override the system behaviour in different directions. The problem is how to coordinate features' access to the service's shared variables. This paper proposes coordinating features via feature composition. We model each feature as a separate labelled-transition system and define a 1conflict-free (CF) composition operator that prevents enabled transitions from synchronizing if they interact: if several features' transitions are simultaneously enabled but have conflicting actions, a non-conflicting subset of the enabled transitions are synchronized in the composition. We also define a conflict- and violation-free (CVF) composition operator that prevents enabled transitions from executing if they violate features' invariants. Both composition operators use priorities among features to decide whether to synchronize transitions.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".