El entrenamiento de los policías para detectar mentiras
Bibliographic record
Abstract
La formacion de la policia en deteccion de mentiras es escasa. Ademas, la Tecnica Reid (la modalidad de entrenamiento para detectar mentiras mas popular en el ambito internacional) se basa en indicadores del engano erroneos segun la investigacion cientifica, parte de creencias de sentido comun, no favorece la discriminacion entre verdades y mentiras y puede generar confesiones falsas. La escasa calidad del entrenamiento en los policias se refleja en que estos coinciden con los no policias en sus creencias (erroneas) sobre los indicios del engano y en sus escasos aciertos al juzgar la veracidad. Es importante entrenar adecuadamente a la policia, pero los programas de entrenamiento usados en estudios de laboratorio apenas incrementan los aciertos y sesgan los juicios hacia la mentira. Esto se debe a su enfasis exclusivo sobre la mentira y sus indicios (y no los de la veracidad). Cerramos el presente trabajo con algunas recomendaciones para el entrenamiento
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".