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.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".