Análisis de Contenido y Bibliométrico de la Deserción Escolar en Instituciones Educativas
Bibliographic record
Abstract
This article aims to identify, through bibliometric analysis, new lines and areas of research, determine the most prolific and cited authors, the core journals, and the institutions conducting the most research on the topic of school dropout. To achieve this objective, 6,407 documents from the Scopus database were reviewed using content and bibliometric analysis with the VOSviewer software. The main findings indicate that most of the generated information in this field comes from the journals PLOS ONE, BMC Public Health, Nurse Educator, and Economics of Education Review. The countries with the highest number of citations are the United States, England, Spain, Germany, and Canada. Meanwhile, Universidad Complutense de Madrid, Universidad de Oviedo, Stanford University, University of Toronto, Graz University of Technology, and Teachers College Columbia University are the institutions with the highest affiliation of publications. The keyword analysis of the literature related to school dropout reveals five main research clusters: Education, School Dropout, Academic Performance, Curriculum, and Student Attrition as the most relevant trends. Therefore, this study makes a significant contribution to the literature by providing a framework for future research, offering opportunities for researchers to explore the network of relationships among research streams.
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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 | 0.028 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.181 | 0.211 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".