Research Data Management Training Landscape in Canada : A White Paper
Bibliographic record
Abstract
This White Paper provides a high-level perspective on RDM training for Portage. RDM developments in Canada have lagged behind some of the countries typically considered to be our peers, such as the United Kingdom and the United States. This was evident in our environmental scan of the different training activities being developed and offered. Some excellent international training modules are available to Canadian stakeholders but without Canadian-specific content. The Portage website provides an opportunity to prepare and disseminate materials rich in Canadian RDM content. RDM expertise already exists in Canada. However, this expertise remains largely siloed in specific disciplines and jurisdictions. Training resources need to be organized collaboratively across these divisions to capitalize on the knowledge and resources of these stakeholder communities. While our overview of RDM training is not exhaustive, it does provide a robust representation of the current landscape. It is also imperative that in building a foundation for RDM expertise, a national research data culture is also cultivated that represents the underlying principles and values of such expertise in Canada.
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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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".