Bibliographic record
Abstract
These are the slides from the LIBER 2021 Session: Knowledge Café Description: This year we would like to ask for your input on the new strategy that LIBER will develop for the 2023-2027 period. With LIBER’s current Strategy (2017-2022) coming to a close, we would like to invite our members to join us in our effort to think back and assess LIBER’s efforts since 2017 (being at the forefront of representing research libraries in Europe and advocating for the future of Open Science). 2020 was undoubtedly a rocky and unpredictable year and LIBER, more so than before, ensured the delivery of services to our wide network while also running our virtual events. This enabled us to reach every corner of Europe and to especially invite members of underrepresented areas to join our online activities (which was not always possible for them before). As we are currently developing the foundation for our next LIBER Strategy, we invite all conference participants to join us during this Knowledge Cafe and help shape the vision for the next LIBER Strategy. According to LIBER President, Jeannette Frey, “Our network is the biggest source of valuable information when it comes to strategising. The needs and wishes of our network must be addressed as part of this crucial procedure to create a new LIBER strategy. And of course, the new strategy is so much more than simply a document. It must be a living and breathing document that we can all utilise and refer to. As such, we would gladly appreciate your input in this interactive session/Knowledge Café on the topic.”
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.817 | 0.677 |
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".