Bridge2Hyku: Meeting Practitioners’ Needs in Digital Collection Migration to Open Source Samvera Repository
Bibliographic record
Abstract
The University of Houston Libraries, in partnership and consultation with numerous institutions, was awarded an Institute of Museum and Library Services (IMLS) National Leadership/Project Grant to create the Bridge2Hyku (B2H) Toolkit. Content migration from proprietary systems to open source repositories remains a barrier for many institutions due to lack of tools, tutorials, and documentation. The B2H Toolkit, which includes migration strategies, migration tools, as well as system requirements for transitioning from CONTENTdm to Hyku, acts as a comprehensive resource to facilitate repository migration. Through a phased toolkit development process, the project team solicited inputs and feedback from peer migration practitioners via survey and pilot testing. The analysis of the feedback data was built into use cases which informed the development and enhancement of the migration strategies and tools. Working across institutions with migration practitioners’ needs in mind, the project team was able to successfully release a Toolkit that mitigates migration barriers and fills gaps in the migration process. Providing a path to a community-supported open source digital solution, the Bridge2Hyku Toolkits ensures access and expanded use of digital content and collections of libraries and cultural heritage institutions.
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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 | 0.049 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".