C: Adding modern programming language features to C
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it