Los métodos mixtos en la investigación en educación: hacia un uso reflexivo
Bibliographic record
Abstract
RESUMEN Este artículo pretende contribuir al uso reflexivo de los métodos mixtos, es decir, aquellos métodos que combinan en una misma investigación las miradas cuantitativas y cualitativas en vistas de la realización de la fase empírica del estudio. Luego de describir sus características generales y sus principales críticas, se presentan los elementos mínimos que consideramos deberían ser explicitados cuando se emplean los métodos mixtos, con la finalidad de asegurar una vigilancia investigativa y una coherencia epistemológica. Posteriormente, se presenta una ilustración de la operacionalización y aplicación de los métodos mixtos en el caso de una investigación en educación que aborda las dificultades profesionales emergentes en el trabajo cotidiano de los profesores de especialidades agropecuarias.
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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 | 0.102 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".