Archives as a trusted third party in maintaining and preserving digital records in the cloud environment
Bibliographic record
Abstract
Purpose This paper aims to present a case wherein a public archive intervenes in maintaining and preserving digital records (including underlying technological infrastructure) created by a private company to protect the trustworthiness of records, thereby helping the company to discharge their accountability. Design/methodology/approach This paper details the intervention of Tianjin Municipal Archives in the management of the records of Tianjin Otis Elevator Co., Ltd, the technical infrastructure that enables and supports such configuration, the issues encountered and the theoretical implications of this case. Findings This case suggests that not only does the concept of archives as a trusted third party remain relevant in the changing technological environment but also, in certain cases (e.g. wherein the supplier of evidentiary documents holds a monopoly over an industry), archives are becoming increasingly critical in maintaining the reliability and authenticity of digital records in the cloud environment. Research limitations/implications Given the challenges raised by the emerging cloud environment, it is vital to develop a renewed understanding of the concept of archives as a trusted third party, the relationship between archives and commercial third party services and the relationship between public archives and private records. Furthermore, this case identifies the need to re-examine archival methodologies to protect the authenticity of structured data. Originality/value This case exemplifies how archives can help private organizations address issues related to guaranteeing and demonstrating the evidential nature of digital records and provides empirical evidence for archives being conceptualized as a trusted third party in maintaining and preserving digital records.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".