Records storage in the cloud: are we modelling the cost?
Bibliographic record
Abstract
Cloud services are increasingly seen as a flexible strategy for platform, infrastructure and software. Given the cloud’s claimed economic benefits archives and records professionals are now using cloud services for the storage of digital records and data. However, in determining whether or not to use the cloud for records and/or data storage, what models are available to them for estimating the cost and the medium-to-long-term financial implications for their organisations? This article identifies models available for estimating cloud storage costs and presents the results of an international survey into their use in the decision-making process with a series of real use case examples illustrating their value. The study highlights a series of important implications for archivists and records managers. These include the importance and challenges of using the models, their lack of widespread use, their adequacy, and the multiple players who should be involved in their application and development. Archivists and records managers need greater awareness and understanding of the models so they can play a central role in the cloud storage decision-making process and in the development of more effective costing models.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".