Metadata Driven Tools Developed for the Canada Research Data Centre Network
Bibliographic record
Abstract
Over the past 5 years the Canadian RDC Network in partnership with Statistics Canada has developed a metadata driven catalogue for its data collection which will be incorporated into a soon to be developed Data Management tool for their newly centralized data repository. The goal of this project was to create a suite of tools with which the data and metadate are managed more efficiently and researchers can discover the collection more easily by exploiting machine actionable applications. The tools developed include a RDC Metadata editor tool in DDI3.1, an ingester tool to convert DDI2 to DDI3, a researcher discovery tool as well as a conversion tool which converts Statistics Canada metadata to DDI3. This presentation will focus on the workflow used to populate the metadata catalogue, the tools developed for the process of building the metadata catalogue as well as the tools developed that researchers will be able to use to discover the data and metadata. We will also discuss lessons learned throughout the project and what our next steps are.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".