EOSC-Pillar Use case 5 - FAIR principles in data life-cycles for Humanities
Bibliographic record
Abstract
This task aims to identify and develop use cases based on Social Sciences and Humanities (SSH) communities engagement. In order to do that, we will rely on consortia funded by Huma-Num and other partners from DARIAH (e.g. Italy and Germany). Another focus will be done on the link between data and publication. HAL, the French national open archive created by CCSD, provide a specific portal for SSH communities to deposit and deliver their publications in open access. CCSD will work with Huma-Num to link publications on HAL to research data in data repositories, especially Nakala, the data repository from Huma-Num dedicated to SSH. This case will be a model for linking with other data repositories used in SSH communities. The first step of building the relationship between the publications deposited in HAL and the data deposited in Nakala has been taken, using the APIs available in each of the repositories. The relations thus created will be displayed, exported and harvestable. In order to be able to synchronise the relationships established in both platforms, we did the technical choice of using the MERCURE protocol and installed a MERCURE server for this purpose. Then the next steps are to be able to ensure that the links made manually between data and publications can be synchronised and shared at will. Learn more
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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 | 0.049 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.017 |
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".