Inteligencia artificial: impactos y desafíos en las contrataciones públicas. Revisión sistemática
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial Intelligence (AI) has generated diverse impacts and challenges in public procurement processes, with its application being fundamental for economic development and social inclusion. This is justified by the persistence of corruption, regulatory barriers, and the lack of state sustainability. The objective was to analyze factors that affect efficiency, transparency, inclusion, sustainability, technological innovation, regulations, and internal economic development. The methodology applied a systematic review based on articles indexed in Scopus, using thematic, geographic, and language filters, reaching 50 relevant studies from countries such as Brazil, the United States, Canada, Peru, Mexico, and others. The results revealed that AI identified the centralization of powers that limits the transparency and efficiency of public spending. Corruption was a structural problem in Latin America, while in the US, it demonstrated transparency and cost sustainability, achieving successful initiatives. AI, as part of technological innovation, improved efficiency, although it faced implementation challenges, managing to reach the conclusion, on the grounds for development, reducing regulatory obstacles that limited its effectiveness in public management.
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.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it