Factores socioeconomicos que influyen en la deserción de los estudiantes de la universidad cesar vallejo del distrito de chiclayo, provincia de chiclayo, region lambayeque en el año 2014.
Bibliographic record
Abstract
The university dropout problem is complex and includes a variety of causes, so in this thesis SOCIOECONOMIC FACTORS AFFECTING THE VOID OF STUDENTS AT THE UNIVERSITY OF THE DISTRICT OF CESAR VALLEJO Chiclayo, Chiclayo Province, LAMBAYEQUE REGION IN THE YEAR 2014 was fails to describe the reasons for the phenomenon of university dropout Cesar Vallejo University of Ottawa Branch. The sample consisted of 160 young people who left school; their ages ranged between 16 and 25 years later; of these, 36.25% were female and 63.75% were male. The results show that students who have dropped belong to the first academic cycle, and that the first serious attrition factor underachievement, followed by economic difficulties and family difficulties, among others. In this situation strategies to reduce college dropout, in which each stakeholder group (university, family and student), has great responsibility and task of collaborating in the implementation is proposed. Strategies that support be considered for college because more players than spectators are required to meet the challenges, to assume the responsibilities and opportunities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".