DDI 4 Core: Describing and managing data for traditional and modern data platforms
Bibliographic record
Abstract
Following the DDI 4 Prototype review, DDI 4 Core was launched early this year to address the feedback received and to include new requirements from an ever-evolving data space. DDI 4 Core has a narrower scope than the DDI 4 Prototype had and an emphasis in short-term delivery. The specification, to be released early next year, will support data description in different formats, from traditional wide, unit data files to data cubes, key-value pairs and other advanced data structures prevalent in modern data platforms. It will also include conceptual aspects of variables and classifications as well as the ability to describe data management and lineage in real-world use cases. DDI 4 Core is a production-ready version with an XML representation, to enable interdisciplinary, cross-domain and interoperable metadata-driven solutions for complex data processing, like those existing in National Statistical Offices and other government agencies. This presentation will provide an overview of the specification to date and a vision for the future.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".