Improving Government Enterprise Architecture Practice--Maturity Factor Analysis
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
Recognized as a critical factor for the whole-of-government capability, many governments have initiated Enterprise Architectures (EA) programs. However, while there is no shortage of EA frameworks, the understanding of what makes EA practice effective in a government enterprise is limited. This paper presents the results of empirical research aimed at determining the key factors for raising the maturity of the Government Enterprise Architecture (GEA) practice, part of an effort to guide policy-makers of a particular government on how to develop GEA capabilities in its agencies. By analyzing data from a survey involving 33 agencies, the relative importance of factors like top management commitment, participation of business units and effectiveness of project governance structures on the maturity of the GEA practice was determined. The results confirm that management commitment and participation of business units are critical factors, which in turn are influenced by the perceived usefulness of the GEA efforts.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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