Co-circular RDM: A pilot service for graduate students at the University of Toronto
Bibliographic record
Abstract
The University of Toronto Libraries (UTL) designed a co-curricular Research Data Management (RDM) workshop aimed at introducing graduate students to RDM principles and best practices. This poster will outline our method of developing the workshop and will detail the preliminary results gathered through student feedback. Findings presented will include the domains and divisions expressing interest in such a workshop and what RDM facets or areas of support have increased demand for RDM services at the University of Toronto. Envisioned as part of a larger initiative to supplement gaps in graduate professional skills training and resources, this workshop is an experiment in linking instruction and RDM service development in a large, distributed research university. Key areas covered include a research data overview and best practice pointers for collecting, describing, storing and sharing research data, with an emphasis on creating sound data management plans. Graduate students also learn about emerging research data policies in Canada, as well as RDM requirements implemented by other funding agencies and publishers. This workshop is being offered through the University of Toronto's Graduate Professional Skills (GPS) program, which provides graduate students with training in areas such as teaching and advanced research for co-curricular credit on their transcript.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".