Tracing the Past, Predicting the Future: A Systematic Review of <scp>AI</scp> in Archival Science
Bibliographic record
Abstract
ABSTRACT The rapid expansion of content presents significant challenges in records management, notably in retention and disposition, appraisal, and organization. Our study highlights how integrating artificial intelligence (AI) into archival science can help address these issues. We begin with a thorough analysis of 45 papers published between 2011 and 2023 that met our predetermined criteria. All the articles were written in English; 40% of these were reviews, and the remaining 60% were original research articles. We investigated the key AI techniques and their applications in archives and records management functions. Our findings highlight key AI‐driven strategies that promise to streamline recordkeeping processes and improve data retrieval in the immediate future. This review outlines the current state of AI in archival science and records management and lays the groundwork for integrating new techniques to transform archival practices. Our research emphasizes the necessity for enhanced interdisciplinary collaboration between AI experts and archival professionals.
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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 | 0.049 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.030 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".