Government Cloud Computing Strategies:\nManagement of Information Risk and Impact on\nConcepts and Practices of Information Management
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.
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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 | 0.039 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".