Scalable decision support for digital preservation: an assessment
Bibliographic record
Abstract
Purpose – This article aims to evaluate a new architecture for scalable decision-making and control in preservation environments for its ability to address five key goals: scalable content profiling; monitoring of compliance, risks and opportunities; efficient creation of trustworthy plans; context awareness; and loosely coupled preservation ecosystems. Scalable decision support and business intelligence capabilities are required to effectively secure content over time. Design/methodology/approach – We conduct a systematic evaluation of the contributions of the SCAPE Planning and Watch suite to provide effective and scalable decision support capabilities. We discuss the quantitative and qualitative evaluation of advancing the state of art and report on a case study with a national library. Findings – The system provides substantial capabilities for semi-automated, scalable decision-making and control of preservation functions in repositories. Well-defined interfaces allow a flexible integration with diverse institutional environments. The free and open nature of the tool suite further encourages global take-up in the repository communities. Research limitations/implications – The article discusses a number of bottlenecks and factors limiting the real-world scalability of preservation environments. This includes data-intensive processing of large volumes of information, automated quality assurance for preservation actions, and the element of human decision-making. We outline open issues and future work. Practical implications – The open nature of the software suite enables stewardship organizations to integrate the components with their own preservation environments and to contribute to the ongoing improvement of the systems. Originality/value – The paper reports on innovative research and development to provide preservation capabilities. The results of the assessment demonstrate how the system advances the control of digital preservation operations fromad hocdecision-making to proactive, continuous preservation management, through a context-aware planning and monitoring cycle integrated with operational systems.
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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 | 0.056 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".