C: Adding modern programming language features to C
Bibliographic record
Abstract
Summary The C programming language is a foundational technology for modern computing with millions of lines of code implementing everything from hobby projects to commercial operating systems. This installation base and the programmers producing it represent a massive software engineering investment spanning decades and likely to continue for decades more. Nevertheless, C, which was first standardized almost 30 years ago, lacks many features that make programming in more modern languages safer and more productive. The goal of the C project (pronounced “C for all”) is to create an extension of C that provides modern safety and productivity features while still ensuring strong backward compatibility with C and its programmers. Prior projects have attempted similar goals but failed to honor the C programming style; for instance, adding object‐oriented or functional programming with garbage collection is a nonstarter for many C developers. Specifically, C is designed to have an orthogonal feature set based closely on the C programming paradigm, so that C features can be added incrementally to existing C code bases, and C programmers can learn C extensions on an as‐needed basis, preserving investment in existing code and programmers. This paper presents a quick tour of C features, showing how their design avoids shortcomings of similar features in C and other C‐like languages. Experimental results are presented to validate several of the new features.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".