Nivel de impacto de la campa??a social "ni una menos" en los aspectos cognitivos y actitudinales de la poblacion femenina del sector central del distrito la esperanza - Trujillo 2016
Bibliographic record
Abstract
La presente investigaci??n parte de las cifras recientes de la prevalencia mundial indican \nque alrededor de una de cada tres (35%) mujeres en el mundo han sufrido violencia, y \ndonde la mayor??a de estos casos son violencia infligida por la pareja. Decidimos trabajar \nen el Distrito de la Esperanza, ya que seg??n el Centro de emergencia de la Mujer \n(CEM), es el distrito que m??s denuncias ha recibido en el a??o 2015, a nivel regional. \nEl objetivo principal de esta investigaci??n es determinar el nivel de impacto de la \ncampa??a ???Ni Una Menos??? en los aspectos cognitivos y actitudinales de la poblaci??n \nfemenina de 15 a 44 a??os del Sector Central del distrito la esperanza en Trujillo, \nEl dise??o de contrastaci??n de esta investigaci??n es descriptiva-evaluativa, ya que se \nestudi?? la campa??a social ???Ni una menos??? y el impacto en los aspectos cognitivos y \nactitudinales. \nSe realizaron entrevistas a la directora del CEM - La Esperanza y a dos voceras de la \nCampa??a Social ???Ni Una Menos??? en Trujillo. Adem??s, se aplic?? encuesta a una muestra \nde 381 mujeres del sector Central del distrito La Esperanza. \nSe concluye que la campa??a tuvo un alto impacto en la poblaci??n femenina del sector \nCentral del Distrito La Esperanza, ya que conocieron la tem??tica de la campa??a, muchas \nde ella participaron, entendieron el mensaje que se quiso transmitir, est??n motivadas a \ncompartir la informaci??n para erradicar la violencia en su sector.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".