FAIR SSH Data Citation: A Practical Guide
Bibliographic record
Abstract
Data citation in Social Sciences and Humanities (SSH) can be a rather complicated task, in particular when it comes to making it machine actionable. The incompleteness of existing citation methods and complexity of the technical landscape only add to the challenge. To address this problem, after doing an inventory of citation practices SSHOC project T3.4 set out to create recommendations and software to: make SSH data-sets citable; visualise and exploit citations; provide facilities for curation and semantic annotation of these resources. This webinar will focus on practical aspects of SSH citation 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.
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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 | 0.062 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.098 | 0.086 |
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".