El desarrollo moral y la toma de decisiones éticas del psicólogo
Bibliographic record
Abstract
En una disciplina con las características de la psicología, la ética profesional es fundamental. Las universidades, asociaciones, colegios y otros tipos de organizaciones de psicólogos alrededor del mundo consideran de gran relevancia a la ética para la formación y el ejercicio de la profesión. Para clarificar los procesos involucrados en el comportamiento ético profesional, en las últimas décadas surgen diversos modelos de toma de decisiones éticas, principalmente en disciplinas como medicina, enfermería y el derecho, entre otras.
 La posibilidad de poner en práctica un proceso de toma de decisiones éticas requiere que las personas presenten ciertas habilidades que han sido investigadas por diversos autores, dando lugar al constructo de “razonamiento moral”. En este artículo se reporta el análisis de las características de desarrollo del razonamiento moral que un psicólogo requiere para cumplir con las exigencias de la ética profesional desde la perspectiva de las principales teorías que lo han abordado.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.012 |
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; both teacher heads agree on what is shown here.
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".