Linking university social responsibility programs to comprehensive student training in the Peruvian context
Bibliographic record
Abstract
University Social Responsibility (USR) programs are closely linked to comprehensive student education, as they integrate academic learning with ethical, social, and civic development; these programs not only strengthen students' technical knowledge, but also promote values such as solidarity, commitment, and empathy by involving them in community actions and projects oriented to the common good; This study sought to establish the degree of connection between USR and comprehensive university education. A basic type study with a quantitative approach and correlational level, the survey technique and the instrument were used, the questionnaire called University Social Responsibility and Comprehensive Student Education. The population consisted of 221 students from the Faculty of Engineering of a public university in the department of Junín - Peru , the inclusion criterion being enrolled in the 2025-I academic period. The data were processed through structural equations (SEM); Within the results, we can see a Spearman Rho correlation coefficient of 0.831 with a significance level of 0.001, which demonstrates a positive and strong association between the variables studied. Furthermore, the general hypothesis of a significant relationship between University Social Responsibility and the comprehensive development of students is validated.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.005 |
| 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".