Deserción en una Universidad del Sureste Mexicano, Valoración de las Causas
Bibliographic record
Abstract
School dropout is a problem that occurs at different educational levels. Different factors influence it: academic, family, social, economic, health, among others; these have a significant impact on the permanence of students. For Higher Education Institutions (HEIs), it is a phenomenon that must be addressed with responsibly, through the design of educational strategies that allow for the timely graduation of students. This research aims to assess the causes of school dropout by identifying the most frequent factors that occur in students at an HEI in southeastern Mexico; to generate recommendations to reduce the frequency of this phenomenon. The methodology used was a quantitative approach, defining the universe to be studied as eighth and ninth 8th quarter students (May-August 2025) of bachelor’s programs in: Business and Project Management, Business Innovation and Marketing, and Accounting (110 students); A sample of 29 students was determined, using the t-Student distribution. The results obtained showed that some of the factors that contribute to school dropouts correspond to: failure, educational model, family problems and lack of financial support (scholarships) from the university.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".