Open Research Europe in practice – All a librarian needs to know
Bibliographic record
Abstract
Open Research Europe (ORE) is a new publishing platform launched by the European Commission. The platform provides all H2020 and Horizon Europe beneficiaries and their collaborators with an easy, high-quality platform to publish Horizon 2020 and Horizon Europe funded research at no cost and in full compliance with the Commission’s open access policies. Building on our first joint LIBER-ORE awareness-raising webinar in October, this follow up event entails a live demonstration of the ORE platform focusing on everything that a research librarian needs to know. Participants will understand how the platform can be used, experience an in-depth walkthrough of the open peer review process, hear about the answers to FAQs that ORE has received so far (the latter may be useful in supporting researchers in publishing via the ORE platform). Participants can also of course ask any questions they might have about the platform. Speakers: Kelly Woods is the Senior Associate Publisher at F1000, a leading Open Research publisher, responsible for looking after Open Research Europe, in collaboration with partners LIBER, Eurodoc and GYA. She has a background in Open Access scholarly publishing and prior to joining F1000 she worked at Frontiers and The Royal Society on numerous OA titles. Matthew Ranscombe is a Senior Assistant Editor in the Prepublications team at F1000, primarily responsible for handling submissions in the humanities and social sciences. He has a background in academic book publishing, previously holding positions at Routledge and Oxford University Press. Joe Kelly is an Associate Editorial Assistant in the Peer Review Team at F1000. Previously he worked in Digital Resources at Taylor & Francis and has a background in research with PhD in history from the University of Liverpool.
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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 | 0.072 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.060 | 0.102 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.022 | 0.018 |
| Insufficient payload (model declined to judge) | 0.057 | 0.065 |
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".