Características y Trayectorias de las Mujeres que Experimentan Situaciones de Calle en Chile
Bibliographic record
Abstract
Este artículo busca contribuir al conocimiento sobre las experiencias de situación de calle de mujeres en Chile, abordando la pregunta sobre ¿Cuáles son las principales características de las experiencias de situación de calle de las mujeres en Chile? Para ello, se utiliza información secundaria del Ministerio de Desarrollo Social y Familia del Gobierno de Chile, actualizada al 31 de marzo de 2019. Se realizaron análisis descriptivos sobre las principales características demográficas y de la vida diaria de las mujeres en situación de calle, y dos modelos de regresión (logística y lineal) que buscan analizar las variables asociadas al tiempo que han experimentado situación de calle y la edad en que lo hicieron por primera vez. A partir de los resultados, se concluye que las variables asociadas a la edad pueden ser predictoras de cómo se van desarrollando las trayectorias de situación de calle de las mujeres.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".