CARL Portage: The RDM journey continues...
Bibliographic record
Abstract
The story of Portage is one of successes, challenges, and hope. As Canada's national, library-based research data management network, the goal of Portage is to coordinate and expand existing expertise, services, and infrastructure in support of academic researchers across Canada. As Portage enters its fourth year of operation, it is useful to reflect upon our journey with stories that illustrate Portage successes, that describe how we're addressing current challenges, and that anticipate, with hope and determination, continued progress toward a collaborative and productive RDM ecosystem in Canada. Speakers in this session will rely upon Portage-sponsored surveys and initiatives, and consultations with stakeholders from across the country, to conduct a retrospective examination of our journey to date in order to share stories of successes, challenges, and gaps in the Canadian RDM ecosystem. Our vision of the future will be informed by these experiences and by emerging policy statements and funding scenarios. Participants will leave with a better sense of what Portage is about, what Portage has accomplished, and what's coming around the next turn in our RDM journey.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.008 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 0.013 |
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".