Assessing digital preservation frameworks: the approach of the SHAMAN projectapproach of the SHAMAN project
Bibliographic record
Abstract
How can we deliver infrastructure capable of supporting the preservation of digital objects, as well as the services that can be applied to those digital objects, in ways that future unknown systems will understand?A critical problem in developing systems is the process of validating whether the delivered solution effectively reflects the validated requirements.This is a challenge also for the EU-funded SHAMAN project, which aims to develop an integrated preservation framework using grid-technologies for distributed networks of digital preservation systems, for managing the storage, access, presentation, and manipulation of digital objects over time.Recognising this, the project team ensured that alongside the user requirements an assessment framework was developed.This paper presents the assessment of the SHAMAN demonstrators for the memory institution, industrial design and engineering and eScience domains, from the point of view of user's needs and fitness for purpose.An innovative synergistic use of TRAC criteria, DRAMBORA risk registry and mitigation strategies, iRODS rules and information system models requirements has been designed, with the underlying goal to define associated policies, rules and state information, and make them wherever possible machine-encodable and enforceable.The described assessment framework can be valuable not only for the implementers of this project preservation framework, but for the wider digital preservation community, because it provides a holistic approach to assessing and validating the preservation of digital libraries, digital repositories and data centres.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".