Examining the Intricate Relationship Between E-Government Readiness and State Fragility
Bibliographic record
Abstract
Digital governance initiatives offer significant potential for enhancing public sector transparency, efficiency, and service delivery. However, fragile states frequently encounter profound barriers to adopting and sustaining e-government reforms. While static indices such as the E-Government Development Index (EGDI) and the Fragile States Index (FSI) offer useful benchmarks, they fail to capture the evolving nature of digital readiness and governance resilience. To address this gap, our study employs Self-Organizing Maps (SOM), an unsupervised machine learning technique, to cluster countries based on 15 EGDI and FSI indicators spanning 2008 to 2022. We identify three distinct developmental groups: Stabilized Nations, Emergent Nations, and Nascent Nations, and observe preliminary transition patterns indicating gradual convergence for some fragile countries. These early insights contribute to a dynamic understanding of the interplay between digital development and governance stability. Managerial implications emphasize sustained investments in digital infrastructure and human capital to enhance institutional resilience. Future work will extend this analysis to case-specific trajectory modeling and predictive transition analysis.
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How this classification was reachedexpand
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".