e-Government architectures, technical and political situation in Latin America
Bibliographic record
Abstract
E-Government architectures started out as management instruments mainly focused on the technical (ICT); side of government. Today, they are developing into tools that map out the business side of government, and link this both to the governance and technology dimensions of government. There is a large consensus on the necessity of building «e-Government » on solid architectures, but no clear definition of what « e-Government architectures » are. The terms architecture, interoperability framework, reference architecture are often confused and used interchangeably. A Framework is rather a list of applications and tools. It provides e-Government interoperability by creating a pool of common tools. Architectures take it one step further by organizing these applications and not only listing them. Interoperability means the ability of information and communication technology (ICT); systems and of the business processes they support to exchange data and to enable sharing of information and knowledge.1 It allows different channels to rely on a common infrastructure to complement each other. It also allows service delivery applications to be independent from the front-end delivery channels. The first part of this paper aims to discuss the various literature referring to e-Gov architectures. The second part looks at the institutional and technical environment by looking at some country case-studies around the world (UK, Germany, France, USA, Canada, Hong-Kong and Singapore);. The third part looks at the technical and political situation of the Latin American environment by looking at a four country-case study (Brazil, Chile, Colombia, Mexico);. Given the heterogeneous scenario on which countries in the region are currently developing their e-Gov architectures, the final part issues some recommendations by pointing out their main characteristics and thresholds to cross.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".