Sharing the RM Toolkit: Panel presentation at Association of Canadian Archivists (video)
Bibliographic record
Abstract
Description of the panel\nUniversities can work together to address common records problems. University archives in Canada house a wide range of publicly accessible material. The majority of Canadian university archives (60%) also have records management responsibilities, working directly with records creators throughout the life cycle of the record. Attendees will learn how B.C. university records managers created a knowledge-sharing group to overcome limited resources and to collaborate on creating innovative solutions to the records problems shared by all members of the group. \n \nThis session, moderated by Barbara Towell of the University of British Columbia, begins with the outcomes from a recent survey of twenty Canadian university records management programs undertaken and presented by Shan Jin of Queens University. This research highlights similarities and areas for increasing collaborations to solve common records problems. Other presenters will detail specific innovative solutions to electronic records issues. Jane Morrison from the University of Victoria Archives will describe how integrated information management work over the past few years has expanded UVic's program and focus on the resources that will benefit the community.\n Joy Rowe from Simon Fraser University will advocate for creating Creative Commons licensed training tools for records creators that are intended to be repurposed, remixed, and shared online, based on recent efforts at SFU.\n \nSession attendees will come away with concrete examples of how informal but regular knowledge-sharing can help professionals facing similar problems in similar institutions achieve their shared goals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.272 | 0.077 |
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".