The Design and Use of Assessment Frameworks in Digital Curation
Bibliographic record
Abstract
To understand and improve their current abilities and maturity, organizations use diagnostic instruments such as maturity models and other assessment frameworks. Increasing numbers of these are being developed in digital curation. Their central role in strategic decision making raises the need to evaluate their fitness for this purpose and develop guidelines for their design and evaluation. A comprehensive review of assessment frameworks, however, found little evidence that existing assessment frameworks have been evaluated systematically, and no methods for their evaluation. This article proposes a new methodology for evaluating the design and use of assessment frameworks. It builds on prior research on maturity models and combines analytic and empirical evaluation methods to explain how the design of assessment frameworks influences their application in practice, and how the design process can effectively take this into account. We present the evaluation methodology and its application to two frameworks. The evaluation results lead to guidelines for the design process of assessment frameworks in digital curation. The methodology provides insights to the designers of the evaluated frameworks that they can consider in future revisions; methodical guidance for researchers in the field; and practical insights and words of caution to organizations keen on diagnosing their abilities.
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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 | 0.342 | 0.412 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".