AfterSSHOC: synergies along the journey to EOSC and a view into the future
Bibliographic record
Abstract
<strong>Moderator:</strong><em> Ivana Ilijasic Versic (CESSDA, SSHOC)</em> Reporting back on Policy Discussions. Collaboration opportunities after SSHOC and Agreements -<em> Franciska de Jong (CLARIN)</em> ESFRI Cluster projects - <em>Giovanni Lamanna (ESCAPE)</em> EOSC Association - <em>Ute Gunsenheimer (EOSC Association)</em> EOSC Future - <em>Ron Dekker (Technopolis Group, EOSC Future)</em> Data Communities - <em>Laura Morales (SciencesPo, SSHOC, Member of EOSC Researcher Engagement & Adoption Task Force, EOSC Future User Group)</em> <strong>Panel discussion with</strong>: <em>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)</em>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".