Una aproximación al gobierno electrónico de los Estados Unidos, Canadá y México en el marco del Tratado de Libre Comercio de América del Norte (TLCAN)
Bibliographic record
Abstract
espanolEn los regimenes democraticos, el e-gobierno es un recurso que contribuye a mejorar la gestion publica. En el marco del TLCAN, se estudian los casos de Estados Unidos (reinventing government), Canada (costumer-focused) y Mexico (e-Mexico). Mientras que en los Estados Unidos y Canada se han logrado avances importantes en materia de e-gobierno, en Mexico los rezagos al respecto son considerables. Cabe acotar que la implementacion de herramientas informaticas e Internet no debe concebirse como un fin en si mismo, sino como un procedimiento para alcanzar objetivos propios de la administracion publica, sobre la que inciden de manera directa las condiciones politicas, economicas, legales, culturales y tecnologicas heredadas e imperantes en cada pais. EnglishIn the democratic regimes, e-government is a resource that contributes to improve public management. Within the NAFTA context, we study United States (reinventing government), Canada (costumer-focused) and Mexico (e-Mexico) cases. Whereas the United States and Canada have obtained important advances in the matter of e-government, the backwards in Mexico are considerable. It is possible to limit that the implementation of computer science tools and Internet does not have to be conceived like an aim in itself, but like a procedure to reach own public administration´s objectives, which are influenced by political, economic, legal, cultural and technological conditions inherits and in use in each country.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".