LIBER 2021 Session #6: The Future is Open: Democratisation of Knowledge
Bibliographic record
Abstract
These are the slides from the LIBER 2021 Session The Future is Open: Democratisation of Knowledge This session will be chaired by Thomas Kaarsted, University Library of Southern Denmark Libraries, Citizen Science and Open (Cultural) Data: a seminal relationship?, Martin Munke, Saxon State and University Library Dresden, Germany Leveraging an open infrastructure to enable visual discovery in library systems: the case of Open Knowledge Maps, Peter Kraker, Open Knowledge Maps; Beate Guba, TU Wien Bibliothek, Austria, Andreas Ferus, University Library, Academy of Fine Arts Vienna, Austria, Andrea Hacker, University Library of Bern, Switzerland, David Johann, ETH Library, ETH Zurich, Switzerland, Najmeh Shaghaei, University Library of Southern Denmark, Denmark, Guido Scherp, ZBW – Leibniz Information Centre for Economics, Germany Speed talk: Early Career Researcher Day: A Case Study in Collaboration Across the University and Beyond, Heather Saunders, Jaime Orr, Brianne Selman, University of Winnipeg, Canada In the first presentation, Martin Munke presents the concept of Open Citizen Science in detail – the Open curation, editing and further processing of digital data and objects provided by GLAM institutions in collaboration between citizen scientists and these institutions. He also discusses its suitability for dealing with the library identity crisis that came with the digital revolution and the profound changes the research landscape underwent as a result of it. The main question asked concerns whether it is viable to claim there is a seminal relationship between libraries, Citizen Science and Open (Cultural) Data. Next, Peter Kraker and Beate Guba discuss Open Knowledge Maps in order to present a novel model that connects them back to library systems and makes it possible to add custom visual discovery services to their offerings. Open Knowledge Maps attempt to transform discovery of scientific knowledge by providing an open, visual, and community-driven system that is based on Open Infrastructure. They provide an instant overview of a field by showing its main areas at a glance and papers related to each area. Based on this idea, in the novel model, Open Knowledge Maps acts as a cloud, so there is no need to install new software on library servers. The presentation explains the workings, and uses, of this tool. Finally, a lightning talk by Jaime Orr, Heather Saunders, and then Brianne Selman will explore Early Career Researcher Day, an inter-university event spearheaded by the University of Winnipeg. Attendees considering how to encourage knowledge sharing among early-career researchers and between academics and their support staff can learn from the case study of this event. The talk will report on the successes and challenges of both the inaugural in-person event in 2020 and the 2021 virtual adaptation, which responded to the unique challenges of the global pandemic.
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 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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.358 | 0.190 |
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".