Living with the Law: Can Automation give us Moore with Less?
Bibliographic record
Abstract
Multi-core programming presents developers with a dramatic paradigm shift. Whereas sequential programming largely allowed the decoupling of source from underlying architecture, it is now impossible to develop new patterns and abstractions in isolation from issues of modern hardware utilization. Synchronization and coordination are now manifested at all levels of the software stack, and developers currently lack the essential tools to even partially automate reasoning techniques and system configuration management. As a first stage to addressing this problem, this paper proposes a framework for a tool suite designed to partially automate the acquisition and management of static system visualization in a feedback loop with dynamic execution properties. This model enables developers to find a best fit system configuration, potentially reconciling resource contention and utilization tensions that are critical to multi-core platforms. The application of a prototype of this suite, Deja View, demonstrates how tool support can aid reasoning about causally related sets of changes across system artifacts.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.010 | 0.035 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".