Making metadata FAIR: Combining DDI solutions with other standards in official statistics
Bibliographic record
Abstract
Metadata in statistical production is ubiquitous: from concepts, classifications and variables to retention and provenance information, metadata is created, used and shared across all phases of the data lifecycle. Unfortunately, metadata management is sometimes silo-based and tool-specific, which impairs all four FAIR principles (Findable, Accessible, Interoperable, Reusable). Statistics Canada is in the early stages of implementing a virtual metadata integration platform, the Metadata Hub, that integrates a collection of metadata repositories, e.g. Colectica, Aria, SDMX Istat Toolkit, OpenLink Virtuoso and CKAN across a number of standards, e.g. DDI, SDMX, XKOS, DCAT, and RDF/OWL, among others. In this presentation we describe our experience so far and the way forward A recorded version of this talk can be found here: https://youtu.be/-90rq_IlxQM
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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 | 0.160 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.033 | 0.061 |
| Open science | 0.008 | 0.033 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".