Integrating DDI-L with ModernStats Models @ StatCan
Bibliographic record
Abstract
The ModernStats models, including GSBPM and GSIM, alongside reference architectures like CSPA and CSDA, provide a robust framework for understanding statistical production, guiding business decisions, and designing reusable software components. However, bridging the gap between these conceptual models and their implementation standards—particularly DDI 3.3 and SDMX—has be-come a focal point of discussion within the international community. This presentation details Statistics Canada’s recent efforts to bridge this gap by deploying a suite of standard-based data and metadata management tools (e.g., Colectica, Aria, Fusion Metadata Registry, Data Lifecycle Manager) aligned with the ModernStat models. The challenge lies in making these tools, developed by different communities, interoperate at a semantic level. Achieving this requires mapping between the standards and developing customized micro-utilities that act as "connective tissue," enabling the creation of data and metadata pipelines across the phases of the GSBPM. We will present key use cases that highlight our progress, describe the challenges we have encountered, and outline the road ahead. This session aims to provide a deep dive into how DDI 3.3 is being leveraged to enhance metadata-driven processes at StatCan, offering valuable insights for those working with or planning to implement similar standards-based approaches.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".