Articles, software, data: An Open Science ethological study
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. Open Science seeks to render research outputs visible, accessible, reusable. The Open Science framework is currently evolving vigorously due, among others reasons, to the UNESCO Open Science Recommendation adopted in November 2021. In this context, it is relevant to better visualize and describe the relationships that hold among the direct protagonists of this changing landscape: research teams and their research outputs, namely: articles, software and data, as their comprehension will certainly contribute to foster better Open Science practices.Method. In this work we review and describe, through the information collected in a large number of bibliographic references, the current changing trends involving some essential, defining, characteristics and behaviors of the main components of the scientific production, namely, research teams and three kinds of research outputs they produce in many scientific areas. This comparative study is based, among others, in our recent work on the evolving concepts of research software, research data in the context of Open Science.Results. In this work we observe and document some key features in this evolving landscape such as the changing and extended roles of research team members; the need to develop a new citing and referencing culture for articles, but specially for research software and data; the rising relevance of open access (to publications, software, data) policies all over the world; the existence of some barriers and difficulties like the regulations concerning academic research close to industry, or other technological applications; the need to develop standards for the “right to be forgotten”; the need to consider the impact of Open Science costs for less favored communities, countries, institutions...Conclusions. This calls for the urgent need to observe and depict further this changing Open Science ecosystem, and to propose –as we have partially attempted in this work– new concepts to analyze this context as well as to contribute to ongoing research-on-research and to improve the implementation of Open Science practices, in order to foster better ways towards a sound, inclusive and fairer Open Science landscape.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchOpen science Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | MetaresearchOpen science Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it