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Record W6912717599 · doi:10.5281/zenodo.5044764

LIBER 2021 Session #5: How Can Open Infrastructures Support the Role of Research Libraries?

2021· article· en· W6912717599 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)Public domainDanishDeliverableOpen researchPerspective (graphical)

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.916

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0140.009
Open science0.0020.012
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.3580.182

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.

Opus teacher head0.036
GPT teacher head0.263
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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