How to weave domain specific information sources into a large, FAIR data fabric for the Digital Humanities? The use of the Dataverse platform.
Bibliographic record
Abstract
Good data curation and data management is a precondition for any replication of research. Research data and research tools are often intricately coupled. But increasingly, digital methodology in the humanities depends on the combination and re-use of data sources outside of their primary area of collection and curation. As those data sources need to be accessible (on-line) in a distributed manner, a holistic approach to curate them becomes more and more important. In this paper, we discuss how a data repository platform (Dataverse) which by default comes with a generic set of metadata can be adapted for the needs of a specific research community (CLARIN).
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 | 0.178 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.057 | 0.141 |
| Open science | 0.007 | 0.042 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".