Assessing open source software as a scholarly contribution
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.
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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 | 0.265 | 0.533 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.050 | 0.030 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.032 | 0.022 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".