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Record W3158612123 · doi:10.18438/eblip29887

Sustainable Digital Preservation Initiatives Benefit from Multi-Pronged Approach

2021· article· en· W3158612123 on OpenAlexaffvenue
Jordan Patterson

Bibliographic record

VenueEvidence Based Library and Information Practice · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSustainabilityDigital preservationDigital librarySubject (documents)Conceptual frameworkResource (disambiguation)Library scienceSustainable developmentKnowledge managementComputer sciencePolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0030.445
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.221
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2021
Admission routes2
Has abstractyes

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