Evaluación de la contratación pública mediante indicadores en el Gobierno Municipal de Santo Domingo, Ecuador
Bibliographic record
Abstract
Public procurement is the process of implementing a set of policies for improving the quality of life of the population. In Ecuador, a system of indicators is established to evaluate the performance and effectiveness of public procurement processes. to evaluate the public procurement processes of the Municipal Decentralized Autonomous Government of Santo Domingo through the system of management indicators is the overall objective. The study was of mixed modality with emphasis on the quantitative; of nonexperimental, cross-sectional and descriptive type. The empirical methods were documentary analysis and measurement through a system of indicators. An analysis of public procurement indicators for the period 2020 to 2023 was developed, determining that budget execution is 65 per cent on average, well below the national average; in addition, the work is insufficient, but there is more effectiveness in planning needs and developing procurement processes. The indicators during the first quarter of 2024 had a similar behavior to previous years and from the analysis developed a group of improvement actions was proposed. The research showed that public procurement work in the municipality of Santo Domingo has a stable behavior, however the levels of efficiency are still insufficient, so a set of actions were proposed that could improve public procurement processes in the GAD and its associated companies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".