Modernización de la Gestión Pública y Atención a la Discapacidad: Un Enfoque Bibliométrico sobre Innovación y Equidad
Bibliographic record
Abstract
The modernization of public management and the attention to the needs of people with disabilities are crucial topics in contemporary public administration. This bibliometric research aims to analyze how the modernization of public management can improve the attention to people with disabilities. A qualitative methodology at a descriptive level was used, employing bibliometric techniques to examine the scientific production in this area. A total of 973 documents from the Scopus database from 1977 to 2024 were analyzed, using the tools Vosviewer and Bibliometrix. The results reveal a growing interest in the intersection of public management modernization and the inclusion of people with disabilities, with a significant increase in scientific production since 2015. The most frequent terms in the literature include "digital transformation," "electronic government," and "accessibility to health services," highlighting the importance of technological innovation in this field. International collaboration and a multidisciplinary approach are noteworthy, with an international co-authorship rate of 18.19%. The geographical distribution of research is led by the United States, the United Kingdom, and Spain, with prominent institutions such as the University of Montreal and Yale University. Funding from the European Commission and Horizon 2020 underscores the political and economic interest in this area. It is concluded that the modernization of public management, driven by technological innovation and international collaboration, can significantly improve the attention to people with disabilities. The implementation of evidence-based policies and intersectoral cooperation are essential to promote equity and inclusion in public services. This study suggests the need to continue researching and developing policies that facilitate modern and efficient public management.
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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 | 0.033 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.113 | 0.221 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".