AfterSSHOC: synergies along the journey to EOSC and a view into the future
Bibliographic record
Abstract
Moderator: Ivana Ilijasic Versic (CESSDA, SSHOC) Reporting back on Policy Discussions. Collaboration opportunities after SSHOC and Agreements - Franciska de Jong (CLARIN) ESFRI Cluster projects - Giovanni Lamanna (ESCAPE) EOSC Association - Ute Gunsenheimer (EOSC Association) EOSC Future - Ron Dekker (Technopolis Group, EOSC Future) Data Communities - Laura Morales (SciencesPo, SSHOC, Member of EOSC Researcher Engagement & Adoption Task Force, EOSC Future User Group) Panel discussion with: Franciska de Jong (CLARIN), Giovanni Lamanna (ESCAPE), Rudolf Dimper (PANOSC), Ute Gunsenheimer (EOSC Association), Ron Dekker (Technopolis Group, EOSC Future) and (Laura Morales, SciencesPo, SSHOC, Member of EOSC Researcher Engagement & Adoption Task Force, EOSC Future User Group)
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 | 0.032 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.071 | 0.012 |
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".