Depicting Dragomans: Islandora for Flexible, Web-based Scholarly Resources
Bibliographic record
Abstract
At the University of Toronto's Scarborough Library (UTSC) the Digital Scholarship Unit (DSU) a small, core technical team is tasked with developing and managing an infrastructure that supports multiple, diverse digital collections and digital scholarship projects. It is a constant challenge to meet the diverse needs of faculty partners and researchers without amassing unmanageable technical debt in the form of highly-individualized web applications that can cause challenges for maintenance and web-archiving longer-term. A decade-long collaboration between digital-humanities researcher and historian Dr. Natalie Rothman (UTSC Professor and Chair of the Department of Historical and Cultural Studies) and Kirsta Stapelfeldt (Librarian and Head of the DSU) for the Dragoman Renaissance Research platform has provided many opportunities for tackling the challenges of online interoperability, sustainability, and addressing complex research needs with limited resources. By prioritizing robust, iterative data modelling and core research and presentation functions over bespoke interfaces, the project aims to provide ample opportunities for data querying and reuse, while limiting long-term maintenance challenges. This presentation introduces the Islandora-based infrastructure at the UTSC's DSU and presents key features that may be of use to others tasked with supporting and stewarding an increasing number of online digital scholarship projects with limited resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 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".