Análisis del perfil de los servidores policiales instructores de la Escuela de Formación de Policías "Cbos. José Lizandro Herrera Calderón" y su incidencia en la formación de los policías del Ecuador
Bibliographic record
Abstract
The work of police officers around the world is considered as one of the complex professions and also one of he hardest and sacrificed works that we can have, because in holidays when people get together with their relatives, the police officers must work hard and leave their time with their families. For those reasons, the police institutions objectives is to become the best institution around the world with a high credibility between the citizens because its labor to keep the security of all people. The education has become the vertebral column in order to get the success in all the professions and in Ecuador it is important to use all the economic technological and human resources to improve the police and educational processes. Chile, Germany and Canada are countries, which own police institutions with a high recognition around the world, and we can take advantage as knowledge as experience, so we can adapt them to our Ecuadorian reality. We can count with professional workers with a big development inside and outside of our country. In this way, we can give a high quality service to out Ecuadorian land. The study of the this work is based in a comparative analysis about the police educational systems around the world, it allowed to the Policia Nacional identify the best model which can be adapted to our social reality and propone a new model in Ecuador.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".