Modelo de evaluación del comportamiento ciudadano en la Administración electrónica (eGov – CIBEM)
Bibliographic record
Abstract
The process of technological transformation is driving the need to redirect the internal management of public administrations and their interactions with citizens, while adhering to the principles of security, interoperability, and accessibility on a global scale. The true quality of public administrations, in this context, resides in the management of knowledge to enhance electronic services and in fostering citizens’ literacy to ensure their effective use. This paper presents a method based on integrative models and a survey of 630 citizens, which has been employed to assess the level of knowledge, perception and usage that citizens have regarding e-Government, specifically in relation to the electronic processing of administrative procedures. The data are analysed using the correlation analysis method to establish relationships between variables, applying Pearson’s chi-square correlation coefficient and the analysis of descriptive statistics. The model provides adjustment mechanisms to improve its alignment with national and international directives on eGovernment and eAdministration. ARK CAICYT: http://id.caicyt.gov.ar/ark:/s18511740/bn39nlt06
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.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".