Assessing open source software as a scholarly contribution
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
Introduction Academic computer science has an odd relationship with software: Publishing papers about software is considered a distinctly stronger contribution than publishing the software. The historical reasons for this paradox no longer apply, but their legacy remains. This limits researchers who see the open-source software movement as an opportunity to make a scholarly contribution. Expanded definitions of scholarship acknowledge both application and discovery as important components. 1 One obstacle remains: evaluation. To raise software to the status of a first-class contribution, we propose "best practices" for the evaluation of the scholarly contribution of open-source software. Typically, scholars who develop software do not include it as a primary contribution for performance reviews. Instead, they write articles about the software and present the articles as contributions. This conflation of articles and software serves neither medium well. An article describes an original intellectual contribution consisting of an idea, the argument for its importance and correctness, and supporting data. In contrast, software is more often an implementation of prior ideas in a usable form. It bridges the often considerable gap between an idea and the practical application of that idea. The original idea and its implementation represent distinct kinds of contribution. The critical gap is the perceived incomparability of these two contributions. Lacking a concise description adapted to the traditional practices of performance review committees, software is difficult to evaluate as a scholarly contribution and is often relegated to second-class status. We propose a framework for common assessment based on widely accepted definitions of scholarship. Within this general framework, we consider the material and procedures that a performance review committee uses to evaluate a publication. We then describe how software can be summarized in a compatible form of bibliographic citation and supplementary material.
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 | 0.007 | 0.200 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.088 | 0.057 |
| 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