The Evolving Best Practices of DDI in Canada
Bibliographic record
Abstract
Sharing of data has become the norm in academia, thus requiring an infrastructure to manage it. As there are different platforms across which they are shared, interoperability is critical to ensure continued collaboration and data sharing across disciplines and institutions. The Data Documentation Initiative (DDI) standard will be examined regarding its use in Ontario universities and the Microdata Access programs at Statistics Canada. The development and current state of Best Practices and tools to ensure collaboration among the different institutions marking up data will be reviewed, including how the shared infrastructure has reduced the cost of data staging and vastly improved data access not only to our local communities of users, but also to user communities nationally and internationally. Nonetheless, challenges do still exist and these are mentioned as well as potential solutions. Finally, we will discuss the evolving state of best practices and suggest ways to move forward with the partnership of those responsible for tagging datasets.
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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 | 0.176 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.059 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.044 | 0.014 |
| Open science | 0.020 | 0.022 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".