Government Cloud Computing Strategies:\nManagement of Information Risk and Impact on\nConcepts and Practices of Information Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research Problem\nThe objective of this research is to investigate the extent to which the government cloud computing\nstrategies of New Zealand, Australia, the United States, the United Kingdom, Canada and Ireland are\nsupported by defined processes for considering the information risks of shifting to cloud computing,\nand assessing the impact of these approaches on concepts and practices of information\nmanagement.\nMethodology\nThe study undertook a qualitative analysis of published policies, strategies and guidance documents\npublished by regulatory agencies within the target jurisdictions, investigating these documents for\nevidence of a process to assess and manage information risks.\nResults\nThe study provides an assessment of the adequacy of governments’ guidance frameworks in\npreparing government organisations to properly assess the risks, opportunities, and necessary\ncontrols for information in a cloud service.\nImplications\nThe gaps in guidance demonstrated by the study identify opportunities for a more rigorous\nassessments of the effectiveness of information management controls and privacy safeguards\nimplemented by government organisations, and points to characteristics which could be assessed\nagainst in more specific case studies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it