Bibliographic record
Abstract
Building on Statistics Canada’s <em>metadata-driven </em>architectural<em> </em>principle and metadata strategy themes: <em>drive, make available, structure</em> and <em>manage</em>, Picasso is an enterprise solution for statistical data and metadata management. Automated business rules will ensure metadata is gathered uniformly, adhering to common architecture, governance and policy instruments. Picasso, a three-year project launched in 2015, replaces local solutions with a hub for managing metadata for all surveys, administrative files and record linkage projects; a data service centre function for all ‘fit for use’ data files; and enterprise search and discovery using metadata to facilitate reuse of information. New tools and components include a metadata designer with an entity lifecycle management and registration process. The solution architecture is based on a hybrid relational/semantic graph (RDF) core registry and repository with a data model driven by standard vocabularies, e.g. SKOS/XKOS, PROV-O, and reference models, e.g. GSIM, DDI 4 and SDMX. Picasso component and external systems interact with the RDF core via a Data Access Layer and Entity Services to access metadata entities via Common Information Exchange Models. Standard vocabularies and models ensure efficient information exchange internally and to external users through the Agency’s website and Research Data Centres.
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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.148 | 0.063 |
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".