FAIR SSH Data Citation: A Practical Guide
Bibliographic record
Abstract
<strong>Data citation in Social Sciences and Humanities</strong> (SSH) can be a rather complicated task, in particular when it comes to <strong>making it machine actionable</strong>. The <strong>incompleteness of existing citation methods</strong> and c<strong>omplexity of the technical landscape</strong> only add to the challenge. To address this problem, after doing an <strong>inventory of citation practices</strong> SSHOC project T3.4 set out to create <strong>recommendations</strong> and software to: make SSH data-sets citable; visualise and exploit citations; provide facilities for curation and semantic annotation of these resources. This <strong>webinar will focus on practical aspects of SSH citation</strong> based on these recommendations, and more specifically on: the value/necessity of data citation; the “FAIR SSH Citation prototype” and other existing tools; practical advice on how to cite SSH data; a new model for data-based scholarship.
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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".