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
MATLAB is a popular dynamic programming language used for scientific and numerical programming. As a language, it has evolved from a small scripting language intended as an interactive interface to numerical libraries, to a very popular language supporting many language features and libraries. The overloaded syntax and dynamic nature of the language, plus the somewhat organic addition of language features over the years, makes static analysis of modern MATLAB quite challenging. A fundamental problem in MATLAB is determining the kind of an identifier. Does an identifier refer to a variable, a named function or a prefix? Although this is a trivial problem for most programming languages, it was not clear how to do this properly in MATLAB. Furthermore, there was no simple explanation of kind analysis suitable for MATLAB programmers, nor a publicly-available implementation suitable for compiler researchers. This paper explains the required background of MATLAB, clarifies the kind assignment program, and proposes some general guidelines for developing good kind analyses. Based on these foundations we present our design and implementation of a variety of kind analyses, including an approach that matches the intended behaviour of modern MATLAB 7 and two potentially better alternatives. We have implemented all the variations of the kind analysis in McLab, our extensible compiler framework, and we present an empirical evaluation of the various analyses on a large set of benchmark programs.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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