LIBER 2021 Session #5: How Can Open Infrastructures Support the Role of Research Libraries?
Bibliographic record
Abstract
These are the slides from the LIBER 2021 Session How Can Open Infrastructures Support the Role of Research Libraries? This session will be chaired by Maaike Napolitano, KB, National library of The Netherlands Knowledge Graph for a more holistic access of artifacts in Digital Libraries, Fidan Limani, Atif Latif, Klaus Tochtermann, Leibniz Information Centre for Economics, Germany How Open Infrastructure Benefits Libraries, Joanna Ball, Royal Danish Library; Niels Stern, OAPEN; Silvio Peroni, University of Bologna, Italy; James MacGregor, Public Knowledge Project, Canada From principles to reality : How OPERAS Research Infrastructure paves the way to Open Knowledge, Suzanne Dumouchel, CNRS (Huma-Num), France, Emilie Blotière, CNRS (Huma-Num), France, Judith Schulte, Max Weber Stufung, Germany, Tiziana Lombardo, Net7 In the first presentation, Fidan Limani explores the integration of scholarly artifacts from the domain of economics using Knowledge Graphs (KG). An initial version of the KG is presented and discussed, all the while keeping a library perspective on the process. Use cases enabled by this approach are also deliberated on, such as opportunities for researchers to interact with multiple facets of a research endeavour (in terms of research deliverables), cases that involve resource complementarity, or those that involve certain research deliverables across providers or collection origin. A final item to discuss includes the methodology used to design, develop, and maintain the current KG and its future extension. In the second presentation, James MacGregor, Niels Stern, Silvio Peroni and Joanna Ball discuss the benefits of Open Infrastructure for libraries. Libraries benefit from Open Infrastructure, including projects such as the Directory of Open Access Books (DOAB), OAPEN, OpenCitations, and Open Journal Systems (OJS), by receiving access to free content and services that help in establishing quality and discoverability. However, they offer libraries much more than just cost-free alternatives to commercial infrastructures. They are also Open in the sense that they have community-based governance models and opportunities for community input into their future developments and directions. In this presentation , we will hear from three Open Infrastructures currently supported by the SCOSS program – discussing how they involve contributing libraries in their governance. In the third and final presentation, Emilie Blotière and Tiziana Lombardo address two services provided by OPERAS and funded by the European Commission – the Research for Society service, under the COESO project (Swafs call) and the Discovery platform for Social Science and Humanities resources (data and publications, profiles and projects), under the TRIPLE project (INFRAEOSC call). The talk will include an introduction of OPERAS and the two services, a discussion on the interoperability and complementarity between these platforms, and an explanation on how the complementarity facilitates institutional funding.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.358 | 0.182 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".