Initializing Global Objects: Time and Order
Bibliographic record
Abstract
Object-oriented programming has been bothered by an awkward feature for a long time: static members . Static members not only compromise the conceptual integrity of object-oriented programming, but also give rise to subtle initialization errors, such as reading non-initialized fields and deadlocks. The Scala programming language eliminated static members from the language, replacing them with global objects that present a unified object-oriented programming model. However, the problem of global object initialization remains open, and programmers still suffer from initialization errors. We propose partial ordering and initialization-time irrelevance as two fundamental principles for initializing global objects. Based on these principles, we put forward an effective static analysis to ensure safe initialization of global objects, which eliminates initialization errors at compile time. The analysis also enables static scheduling of global object initialization to avoid runtime overhead. The analysis is modular at the granularity of objects and it avoids whole-program analysis. To make the analysis explainable and tunable, we introduce the concept of regions to make context-sensitivity understandable and customizable by programmers.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".