Sustainable Digital Preservation Initiatives Benefit from Multi-Pronged Approach
Bibliographic record
Abstract
A Review of: Masenya, T. M., & Ngulube, P. (2020). Factors that influence digital preservation sustainability in academic libraries in South Africa. South African Journal of Libraries and Information Science, 86(1), 52–63. https://doi.org/10.7553/86-1-1860 Abstract Objective – To define principles for the sustainable management and preservation of digital resources. Design – Survey and literature review. Setting – Academic libraries in South Africa. Subjects – Twenty-two academic institutions in South Africa. Methods – The researchers evaluated four conceptual models of digital preservation and conducted a literature review for the same subject. Informed by these reviews, the researchers developed a questionnaire for South African academic institutions, distributed the questionnaire, and studied the results using statistical analysis software. Main Results – Twenty-two of twenty-seven (81.5%) surveys were returned. Results indicated a broad consensus about which factors were important in sustainable digital preservation; all factors listed received anywhere from 86.3% to 100% agreement among respondents. Conclusion – A proposed conceptual integrated digital preservation model recommends a three-pronged approach to address management-related, resource-related, and technological-related factors in sustainable digital 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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| 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".