Building Trustworthy Digital Repositories
Bibliographic record
Abstract
Building Trustworthy Digital Repositories: Theory and Implementation combines information on both theory and practice related to creating trustworthy repositories for records into one up-to-date source. This book will bring all the credible theories into one place where they will be summarized, brought up to date, and footnoted. Moreover, the book will be international in its scope, and will discuss ideas coming from such important sources as Australia, Canada, and Western Europe. Until about five years ago, there were very few implementation projects in this area. This book brings together information on implementation projects that answer these questions: What is a trustworthy repository for digital records?Who is building these repositories, and what have been the results?How are institutions building or creating these repositories?How are institutions addressing the essential requirement related to the ingest or capture of records?How are institutions automatically and manually capturing essential metadata and audit trails?How are institutions implementing retention and disposal decisions within these systems?How are institutions implementing preservation strategies to ensure that digital objects are accessible over long periods of time?What is the current status of trustworthy repositories, and what will these systems look like in the future?
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 | 0.025 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.027 | 0.054 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".