Quality in optimizing administrative simplification for students at Peruvian public universities
Bibliographic record
Abstract
Public management seeks to balance efficiency and effectiveness to ensure that administrative simplification benefits both institutions and users. This study aims to determine the extent to which service quality optimizes administrative simplification. This is an applied research study, with a quantitative approach and explanatory-correlational level, conducted with 122 students from the Faculty of Animal Husbandry at the National University of Central Peru - Huancayo. Inductive-deductive, experimental, comparative, and statistical methods were used, with a longitudinal design. A questionnaire based on the Technical Standard for Service Quality Management in the Public Sector was administered. The results, analyzed using the Z statistical test with a 95% confidence interval, showed a value of 3.527, accepting the alternative hypothesis (μD > 0). This demonstrates that administrative simplification improved significantly after the implementation of strategies designed to correct weaknesses detected in the diagnosis. Consequently, the improvement in service quality resulted in improved administrative simplification, reducing service times and costs. It is concluded that the implementation of strategic actions led to improved service quality, directly benefiting the student population.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".