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Record W7057766132

La paradoja del fin de la pena y el delito de feminicidio en los distritos de Lima Sur en el año 2022

2023· dissertation· es· W7057766132 on OpenAlexaboutno aff

Bibliographic record

Venuerenati · 2023
Typedissertation
Languagees
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Order (exchange)Quarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

El presente estudio ha tenido como objetivo general: Determinar la relación que existe entre la paradoja del fin de la pena y el delito de feminicidio en los distritos de Lima Sur en el año 2022. El feminicidio es un acto criminal contra las mujeres, puesto que contraviene la moral, libertad, y la dignidad humana. Por ello, el Estado peruano ha incorporado la regulación del delito de feminicidio para hacer frente a esta contingencia, dado que el articulo 108-B, del Código Penal del 2004, ha establecido que, el sujeto transgresor que incurra en este delito será sancionado con una pena privativa de libertad no menor de 20 años. No obstante, la regulación de este delito no ha tenido resultados positivos, ya que de forma permanente se han anunciado diversos casos de feminicidio. Con ello, ha surgido la paradoja e incógnita sobre si este régimen legal cumple o no con su función de tutelar la integridad de las mujeres, en tanto que este problema se ha encontrado en los distritos de Lima Sur. Se estableció un estudio básica pura, un enfoque cuantitativo, un diseño correlacional, no experimental y transversal, para la recolección de datos se aplicó la técnica de la encuesta junto con el instrumento del cuestionario. Se concluyó que, el legislador debe especificar detalladamente el delito de feminicidio para que los justicieros no cometan el error de interpretarlo de una forma distinta, y de esa forma se neutralice la paradoja de la pena ante la esencia del feminicidio.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.000

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.

Opus teacher head0.010
GPT teacher head0.328
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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