Tradition versus Innovation: A Comparison of NARA and ANRI’s Archival Management in the Digital Age
Bibliographic record
Abstract
The United States (U.S.) leads at the forefront of modern archival innovation, while Indonesia advances digital transformation amidst the persistent influence of traditional approaches and conservative perspectives in conventional records management. The National Archives and Records Administration (NARA) in the U.S. and the National Archives of the Republic of Indonesia (ANRI) play key roles. A balanced approach combining tradition and innovation is essential for navigating transitional challenges. This study aims to provide a comparative analysis of archival management practices between The United States’s NARA and Indonesia’s ANRI, examining the interplay of traditional approaches and digital innovation. Using a descriptive qualitative methodology and literature review, the research highlights NARA’s advanced digital archival system and ANRI’s developmental stage, where cultural, policy and infrastructure challenges persist. The findings underline the importance of integrating traditional principles with innovative solutions for sustainable archive management. As both nations navigate the complexities of technological advancement, they prioritize data security and privacy measures.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".