Strategies for Preserving Digital Scholarship / Humanities Projects
Bibliographic record
Abstract
The Digital Scholarship Unit (DSU) at the University of Toronto Scarborough library frequently partners with faculty for the creation of digital scholarship (DS) projects. However, managing completed projects can be challenging when it is no longer under active development by the original project team, and resources allocated to its ongoing maintenance are scarce. Maintaining inactive projects on the live web bloats staff workloads or is not possible due to limited staff capacity. As technical obsolescence meets a lack of staff capacity, the gradual disappearance of digital scholarship projects forms a gap in the scholarly record. This article discusses the Library DSU’s experimentations with using web archiving technologies to capture and describe digital scholarship projects, with the goal of accessioning the resulting web archives into the Library’s digital collections. In addition to comparing some common technologies used for crawling and replay of archives, this article describes aspects of the technical infrastructure the DSU is building with the goal of making web archives discoverable and playable through the library’s digital collections interface.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.033 | 0.021 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.000 |
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; both teacher heads agree on what is shown here.
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".