Online collaboration and knowledge dissemination for university collections
Bibliographic record
Abstract
Universities and university museums are faced with numerous challenges regarding access to their collections for diverse user communities. In today’s electronic age, a new model has emerged for granting online access to collections. This paper will present some challenges and successes of current solutions in use by Carleton University’s Great Lakes Research Alliance for the Study Aboriginal Arts and Culture (GRASAC), the University of Pennsylvania Museum collaborative virtual exhibition Tipatshimuna and the McCord Museum, formerly administered by McGill University. The research and knowledge dissemination mandate of universities, together with the issues surrounding the repatriation of objects, online collaboration and access to collections, the digital form presents a real opportunity to benefits universities, researchers, students and the general public.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".