Developing a Library Archive for Secure Data Storage
Bibliographic record
Abstract
In 2019, Western Libraries was asked about potential storage options for data from a major multinational study that had been stagnating on a departmental server for a number of years. The idea of a secure library data archive was floated but despite support from Library Administration and the provision of storage space, we became mired down in endless conversations about procedure, ethics and data ownership. In 2022 an additional data crisis arose: our Research Ethics Board’s former recommended option for sharing sensitive data was being decommissioned and an unknown number of potentially restricted datasets were about to become homeless. That was the push needed and in 2023 the Library Secure Data Archive was launched. Come hear a story of data neglect, peculiar storage recommendations and The Form That Took Forever!
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 | 0.057 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.026 | 0.042 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.017 |
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".