The CESSDA Vocabulary Service: A New State-of-the-Art Tool for Creating and Publishing Controlled Terms Lists
Bibliographic record
Abstract
The DDI Alliance has been creating and publishing its own controlled vocabularies since 2005. These are targeted for specific DDI classes, but are external to DDI and may be used with other metadata standards. In its new core metadata model, CESSDA recommends the use of DDI controlled vocabularies where available, as well as the creation of new lists, as needed. To support this effort, CESSDA has financed the development of a state-of-the-art tool that facilitates the creation, translation and publication of controlled vocabularies and automates a significant part of the process. The tool is now in its final testing phase and is scheduled to go live this spring. Our poster will introduce this new web-based tool in the context of our ongoing controlled vocabularies work and will highlight some of its most prominent features, such as cross-vocabulary searches, group sharing and editing, support for multiple language translations, automated versioning, one-click publishing, and multi-format downloads.
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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.048 |
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".