Future of Scholarly Communication . Forging an inclusive and innovative research infrastructure for scholarly communication in Social Sciences and Humanities
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
This report discusses the scholarly communication issues in Social Sciences and Humanities that are relevant to the future development and functioning of OPERAS. The outcomes collected here can be divided into two groups of innovations regarding 1) the operation of OPERAS, and 2) its activities. The “operational” issues include the ways in which an innovative research infrastructure should be governed (Chapter 1) as well as the business models for open access publications in Social Sciences and Humanities (Chapter 2). The other group of issues is dedicated to strategic areas where OPERAS and its services may play an instrumental role in providing, enabling, or unlocking innovation: FAIR data (Chapter 3), bibliodiversity and multilingualism in scholarly communication (Chapter 4), the future of scholarly writing (Chapter 5), and quality assessment (Chapter 6). Each chapter provides an overview of the main findings and challenges with emphasis on recommendations for OPERAS and other stakeholders like e-infrastructures, publishers, SSH researchers, research performing organisations, policy makers, and funders. Links to data and further publications stemming from work concerning particular tasks are located at the end of each chapter.
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.021 | 0.071 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.000 | 0.002 |
| 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