Assessing Digital Preservation Capabilities Using a Checklist Assessment Method: Paper - iPRES 2012 - Digital Curation Institute, iSchool, Toronto
Bibliographic record
Abstract
Digital preservation is increasingly recognized as a need by organizations from diverse areas that have to manage information over time and make use of information systems for supporting the business.Methods for assessment of digital preservation compliance inside an organization have been introduced, such as the Trustworthy Repositories Audit & Certification: Criteria and Checklist.However, these methods are oriented towards repository-based scenarios and are not geared at assessing the real digital preservation capabilities of organizations whose information management processes are not compatible with the usage of a repository-based solution.In this paper we propose a checklist assessment method for digital preservation derived from a capability-based reference architecture for digital preservation.Based on the detailed description of digital preservation capabilities provided in the reference architecture, it becomes possible to assess concrete scenarios for the existence of capabilities using a checklist.We discuss the application of the method in two institutional scenarios dealing with the preservation of e-Science data, where clear gaps where identified concerning the logical preservation of data.The checklist assessment method proved to be a valuable tool for raising awareness of the digital preservation issues in those organizations.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.961 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".