Bibliographic record
Abstract
Using the extended model for view-centric reasoning, EVCR, we focus on the many possibilities for concurrent processes to be composed. EVCR is an extension of VCR, both models of true concurrency; VCR is an extension of CSP, which is based on an interleaved semantics for modeling concurrency. VCR, like CSP, utilizes traces of instantaneous events, though VCR permits recording parallel events to preserve the perception of simultaneity by the observer(s). But observed simultaneity is a contentious issue, especially for events that are supposed to be instantaneous. EVCR addresses this issue in two ways. First, events are no longer instantaneous; they occur for some duration of time. Second, parallel events need not be an all-or-nothing proposition; it is possible for events to partially overlap in time. Thus, EVCR provides a more realistic and appropriate level of abstraction for reasoning about concurrent processes. With EVCR, we begin to move from observation to the specification of concurrency, and the compositions of concurrent processes. As one example of specification, we introduce a description of I/O-PAR composition that leads to simplified reasoning about composite I/O-PAR processes.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".