Measuring the Impact of Digital Collections
Bibliographic record
Abstract
Assessing content use and reuse is a considerable challenge for gallery, library, archives, museum, and repository (GLAMR) digital library practitioners. While a number of digital object content use studies focus on quantitative approaches to assessment, including digital object downloads, views, and visits, little research has investigated the ways in which digital repository materials are utilized and repurposed. The Digital Content Reuse Assessment Framework Toolkit, or D-CRAFT, addresses some of these gaps by providing assessment methods, ethical considerations and guidelines, tutorials, and "how to" templates to assist practitioners in understanding how digital objects are used and reused by various audiences. The toolkit enhances and advances the typical digital library use assessment approaches. As such, this paper argues that D-CRAFT can play a critical role in assisting GLAMR digital library practitioners in reuse assessment data collection.
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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 | 0.013 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".