Editorial: Bridging Innovation and Heritage in Digital Preservation
Bibliographic record
Abstract
Welcome to Issue 54(2) of Preservation, Digital Technology and Culture, where we present a compelling collection of research that exemplifies the journal's commitment to exploring digital preservation through multiple lensestechnological, social, economic, political, and user-centered perspectives.This issue brings together scholars from across the globe, representing institutions in Indonesia, India, Canada, Ukraine, South Africa, and the United States, demonstrating the truly international scope of our field and the universal importance of preserving our digital and cultural heritage.The seven articles in this issue collectively address some of the most pressing challenges and innovative solutions in digital preservation today.From the application of cloud computing technologies in libraries to the preservation of indigenous cultural heritage, from automated environmental monitoring systems to community-driven archival practices, these contributions reflect the field's evolution toward more sophisticated, inclusive, and sustainable approaches to preservation.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".