Audit DRAMBORA for Trustworthy Repositories: A Study Dealing with the Digital Repository of Grey Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The credibility of a grey literature digital repository can be supported by a specialized audit. An audit of credibility declares that the digital repository is not only a safe place for storage, providing access and migrating to new versions of document formats, it also asserts the care components required of a digital repository environment, including the mandate, typology, policy, team, etc. This audit is very important in showcasing to participants and users the quality and safety of the data process. This paper will present DRAMBORA (Digital Repository Audit Method Based on Risk Assessment), a methodology and tool for auditing a trustworthy digital repository of grey literature. DRAMBORA is an online instrument which helps organizations develop documentation and identify the risks of a digital repository. DRAMBORA is accessible from http://www.repositoryaudit.eu. The paper will also summarize prevailing advantages and disadvantages of DRAMBORA. The second part of this paper will describe the audit of the National Repository of Grey Literature (NRGL) as a trustworthy digital repository using DRAMBORA as part of creating a digital repository of grey literature in the National Technical Library (NTK). The most important outcome of the audit was represented by the identified risks connected to the repository and potentially endangering its operation, quality, image, and other features. The main principle of the DRAMBORA audit and, at the same time, its main contribution, is its iteration (i.e. its repetition after a certain time period in new conditions when the original risks are reassessed; the measurements adopted for solution are assessed and new risks are identified).
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it