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.
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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 | 0.051 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".