Perceptions on the adoption of Free/Open Source Software policies by a Scientific Institution
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
Background. As the Open Science context evolves broadly and Scientific Institutions implement Open Access and Open Science policies, it is of interest to observe which are the issues that come into play when these Institutions attempt to contribute to make their research outputs visible, accessible and reusable. The present work reflects on the problematic behind the establishment and adoption of policies regarding the dissemination of Research Software (RS) as Free/Open Source Software (FOSS) in a Scientific Institution. It takes advantage, as an initial motivation, of the Request for Information (RFI): Best Practices for Sharing NIH Supported Research Software. This request was issued by the USA National Institutes of Health (NIH) organization in October 2023, seeking for responses to a set of questions regarding Research Software sharing and dissemination best practices.Method. In this work we begin by establishing an initial, general framework regarding RS and FOSS fundamental concepts and terminology. Then we include the initial list of questions for the NIH-RFI, followed by our detailed answers, argumented in the context of the provided framework. Results. Besides presenting this large collection of answers, we also include diverse reflections on what we have observed and learned in the context of the analysis of the NIH-RFI, arguing it provides also with the opportunity to explore how the previously published work on RS and Open Science can be envisioned in the lens of the definition and adoption of RS sharing and disseminating policies by any Scientific Institution or research community. Conclusions. We have detailed here a foundational framework, showing how it could be considered as the key tool for providing a systematic reply to the RS and FOSS issues arising in the NIH-RFI. This framework that can be easily adapted to more general contexts, and, thus, could be of potential interest for other scientific organizations, as well as for scientific communities with an important RS production, like the Computer Algebra and the Symbolic Computation ones, aiming to clarify and upgrade their policies and practices regarding this kind of production.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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