Organizational assessment frameworks for digital preservation: A literature review and mapping
Bibliographic record
Abstract
As the field of digital preservation (DP) matures, there is an increasing need to systematically assess an organization's abilities to achieve its digital preservation goals, and a wide variety of assessment tools have been created for this purpose. This article aims to map the landscape of research in this area, evaluate the current maturity of knowledge on this central question in DP and provide direction for future research. To do so, this paper reviews assessment frameworks in digital preservation through a systematic literature search and categorizes the literature by type of research. The analysis shows that publication output around assessment in digital preservation has increased markedly over time, but most existing work focuses on developing new models rather than rigorous evaluation and validation of existing frameworks. Significant gaps are present in the application of robust conceptual foundations and design methods, and in the level of empirical evidence available to enable the evaluation and validation of assessment models. The analysis and comparison with other fields suggest that the design of assessment models in DP should be studied rigorously in both theory and practice, and that the development of future models will benefit from applying existing methods, processes, and principles for model design.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".