Best Practices for Cloud, IT and Digital Infrastructure Programs Across Government Agencies
Bibliographic record
Abstract
Federal governments globally are evolving from a cloud-first policy to cloud-smart. This shift in approach allowed organizations to mature in their cloud journey, which increased the importance and need for the centralized IT cloud infrastructure program. A centralized cloud IT infrastructure program is a common operating model in federal public sector agencies and their service provides agencies and departments with crucial services in the broader cloud adoption and cloud operating journey. Serving agencies and departments as a technology partner is key from an infrastructure perspective, but there is a continued opportunity to serve as a business partner through the advisory lens. When measuring cloud IT infrastructure services, performance indicators and measures should be leveraged and monitored on an ongoing basis. The excellent or poor performance of a cloud IT infrastructure service directly impacts agencies and departments. Such cloud programs in the public sector position governments to be early adopters of innovations like AI, IoT, edge computing, and advanced data analytics. These innovations are almost always tied to clouds, data centers and AI foundational pillars as part of an organization’s digital business technology platform. As agencies embrace digital technologies and increase connectivity, both internally and externally, new vulnerabilities arise, exposing them to cyber threats. To mitigate these risks, cloud IT infrastructure programs must evolve alongside cloud adoption, implementing advanced security measures to stay ahead of emerging threats.
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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 | 0.081 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.034 | 0.021 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".