La utilización de Iramuteq en investigaciones educativas: una perspectiva cualicuantitativa para el análisis de datos textuales
Bibliographic record
Abstract
El presente trabajo académico presenta el estudio realizado por el Grupo de Estudio e Investigación en Política Educativa y Evaluación - GEPPEA, del Programa de Posgrado en Educación de la UEPG, sobre el uso del software IRAMUTEQ para el análisis de datos textuales en investigaciones en el área de educación El programa genera varios informes, entre ellos: el análisis lexicográfico y la nube de palabras, que muestra la frecuencia de palabras en el corpus textual; la Clasificación Jerárquica Descendente (CHD) que identifica varias clases de segmentos de texto y las correlaciones entre ellos; y, el análisis de similitud que presenta las co-ocurrencias entre las palabras y el grado de similitud entre ellas. Todos estos informes generan datos cuantitativos que permiten realizar un análisis cualitativo en la generación de argumentos para sustentar los objetos de estudio en investigaciones y trabajos académicos, constituyendo así una posibilidad de análisis cualitativo y cuantitativo de datos empíricos en la investigación educativa.
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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 | 0.040 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".