Validez de contenido del Cuestionario de Ciberagresión
Bibliographic record
Abstract
El propósito de este trabajo es presentar el proceso de validación de contenido del Cuestionario de Ciberagresión diseñado en Irlanda por Corcoran y Mc Guckin (2014). En este estudio instrumental participaron 15 jueces locales, con experticia y trayectoria en la temática, quienes ponderaron cuantitativa y cualitativamente el cuestionario. Se han tomado en cuenta todas las aportaciones realizadas en el análisis cualitativo. Los datos cuantitativos se sistematizaron utilizando el coeficiente V de Aiken complementado con el uso de intervalos de confianza. Los resultados indican un amplio grado de acuerdo entre los jueces, en la medida en que presentan intervalos de confianza superiores a .50. Por todo ello, se concluye que el Cuestionario de Ciberagresión es una herramienta adecuada para medir dicho constructo en adolescentes escolarizados de Argentina. El presente estudio ofrece el primer instrumento en español válido para medir dicho fenómeno.
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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 | 0.024 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".