Bibliographic record
Abstract
Purpose – Using the example of audiovisual materials, this paper aims to illustrate how records-related and archival legislation lags behind advances in technology. As more audiovisual materials are created on the cloud, questions arise about the applicability of national laws over the control, ownership, and custody of data and records. Design/methodology/approach – This paper analyses court cases relating to audiovisual materials in the cloud and archival legislation from three Commonwealth countries: Canada, Australia, and Singapore – representing North America, the Pacific, and Asia respectively. Findings – Current records-related and archival legislation does not effectively address the creation, processing, and preservation of records and data in a cloud environment. The paper identifies several records-related risks linked to the cloud – risks related to the ownership and custody of data, legal risks due to transborder data flow, and risks due to differing interpretations on the act of copying and ownership of audiovisual materials. Research limitations/implications – The paper identifies the need for records professionals to pay greater attention to the implications of the emerging cloud environment. There is a need for further research on how the concept of extraterritoriality and transborder laws can be applied to develop model laws for the management and preservation of records in the cloud. Originality/value – The paper identifies record-related risks linked to the cloud by analyzing court cases and archival legislation. The paper examines maritime law to find useful principles that the archival field could draw on to mitigate some of these risks.
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 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".