Bibliographic record
Abstract
It is no longer possible to consider exception handling as a secondary issue in language design, or even worse, a mechanism added after the fact via a library approach. Exception handling is a primary feature in language design and must be integrated with other major features, including advanced control flow, objects, coroutines, concurrency, real-time, and polymorphism. Integration is crucial as there are both obvious and subtle interactions between exception handling and other language features. Unfortunately, many exception handling mechanisms work only with a subset of the features and in the sequential domain. A framework for a comprehensive, easy to use, and extensible exception handling mechanism is presented for a concurrent, object-oriented environment. The environment includes language constructs with separate execution stacks, e.g. coroutines and tasks, so the exception environment is significantly more complex than the normal single-stack situation. The pros and cons of various exception features are examined, along with feature interaction with other language mechanisms. Both exception termination and resumption models are examined in this environment, and previous criticisms of the resumption model, a feature commonly missing in modern languages, are addressed.
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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".