"We are the Champions": Exploring a Data Champions Pilot in the Canadian Context
Bibliographic record
Abstract
The Digital Research Alliance of Canada’s (the Alliance) Data Champions Pilot Project is a Canadian funding opportunity built from other international Data Champions programs that came before, and is designed specifically to promote a shift in data culture within the Canadian Digital Research Infrastructure (DRI) ecosystem. The role of the Data Champion (DC) is to develop activities at the local, regional and/or national level that advance awareness, understanding, development, and adoption of research data management (RDM) tools, best practices, and resources in Canada, while focusing on a series of categories: Training/mentoring; Promoting/advancing RDM; Addressing disciplinary challenges; Driving culture change; and Informing future initiatives. Eighteen awardees from Canadian post-secondary institutions, research hospitals, and not-for-profits were selected to be the Alliance’s Data Champions and were funded to undertake their diverse slate of DC initiatives and outcomes. Moderated by Jen Pecoskie (RDM Project Coordinator, the Alliance), this session will explore the Canadian Pilot edition of Data Champions. First, short presentations will be given by the moderator and presenters to provide context on the Alliance’s DC Pilot and to showcase the initiatives of three DC awardees, so the audience will see the range of DC projects. This will be followed by panel questions from the moderator. Panel questions will explore topics such as: What challenges did you experience as part of your DC project experience? What lessons were learned as related to RDM in Canada (or wider) that stemmed from your individual project experience?; Part of the DC funding opportunity included engaging with a DC Community of Practice. What did being part of this wider community bring to your initiative?; and What does the future of your project look like? Questions from the audience will also be incorporated into the panel discussion.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.079 | 0.031 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".