SSHOC Project Brief To support the EC Programme and policy activities
Bibliographic record
Abstract
SSHOC Thematic cluster project [2019-2022] has built a strong and recognisable brand around a consortium of 6 well-established ESFRIs, 7 onboarded Social Science and Humanities (SSH) data communities, an SSH Open Marketplace testers’ community and an SSH Training Community actively breaking down the silos through the sharing of knowledge, tools and services with significant potential for further capacity-building as an important building block for EOSC. Through a solid and continued outreach programme it has engaged over 5000 stakeholders in 65+ events organised, with a social media community of 2200+ members, and 470 newsletter subscribers. Niche scientific communities have been supported by providing 6 additional targeted training events offering a valuable platform to support the onboarding of new communities to present themselves as well as to connect their members from all over the world. The SSHOC website acts as a main communication channel and is the gateway to its array of services including the SSH catalogue of services, connecting its 60+ official reports to each single Key Exploitable Result (KERs) actively consulted by the SSH community (i.e. the System Specification of the SSH Open Marketplace has been downloaded over 4300 times). With the release in December 2021 of the EU Data Governance Act, SSHOC pursues the regulations around the guiding principles of the act to support a Data Science ecosystem in the area of SSH.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.194 | 0.124 |
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".