Three Pillars of the Social Sciences & Humanities Open Marketplace
Bibliographic record
Abstract
SSHOC poster presenting the SSH Open Marketplace at the EOSC-hub Week 2020. The SSH Open Marketplace is the only fully-integrated discovery portal which pools and harmonises the tools and services useful for the SSH research communities, offering a high quality and contextualised answer at every step of the research data life cycle. The poster presents the three pillars on which the SSH Open Marketplace is being built: Integration with existing EOSC services: The SSH Open Marketplace is fully integrated in the EOSC landscape, for example by using the EOSC Federating Core, especially the Federated Identity (AAI) services and the helpdesk. Harmonise views on common themes and foster contact with other organisations operating in the EOSC environment Integration of the marketplace with other EOSC catalogues or marketplaces SSHOC Data and Metadata Interoperability Hub Fostering Open Science in the SSH domain A researcher needs to perform a textual analysis on historical texts: the SSH Open Marketplace will not only offer software and services suitable for the task, but also tutorials explaining how to properly use the software, as well as academic articles and other resources. Discovery portal for the SSH domain Contextualisation to adopt and reuse the content of the SSH Open Marketplace, no matter if they are a piece of software, a dataset, a research workflow or scientific article Centered around the research scenarios of the SSH community in which training materials have a central role to play Community-driven curation The SSH Open Marketplace follows a community-driven approach in building SSH part of EOSC. It is a social infrastructure. Community-based curation Three curation pillars: automatic ingest and update of data sources; continuous curation of the information by the editorial team and - most important - contributions from its users Collaborative and user-centric enhancement of content and context The SSH Open Marketplace is being built at the time of the publication of the poster and will undergo its alpha release in June 2020.
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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 | 0.032 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.033 | 0.026 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.046 | 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".