CATEGORY: Self-pacedAuto-aprendizaje En autonomie

Crime Statistics from a Gender Perspective

Course Access: Lifetime
Course Overview
 

Learning Modality: Self-paced

Date: Available online all year

Duration: Approximately two hours, including the final quiz.

Language: English

Tuition fee: Free

 

{%: Objectives}

In this course you will learn:

  • Understand the importance of incorporating a gender perspective in collecting, producing, analyzing, and disseminating crime statistics.
  • Apply tools and frameworks to improve the production, collection, analysis, and dissemination of crime data from a gender perspective.
  • Analyze the opportunities and challenges of working with various types of data sources.
  • Evaluate the relationship between data, crime, and gender at national, regional, and international levels.
  • Understand recent global initiatives and statistical frameworks aimed at measuring technology-facilitated violence against women (TFVAW) and the gender-related killing of women and girls.
  • Create capacity to produce gender and crime-related SDG indicators using microdata.

{%}  {%: Duration}

Approximately six hours.

{%}  {%: Modules}

  • Module 1: The Need for a Gender Perspective in Crime and Criminal Justice Statistics
  • Module 2: Criminal Acts from a Gender Perspective
  • Module 3: The Gender Perspective in the Criminal Justice System
  • Module 4: Computing SDG Indicators
  • Module 5: Gender-related Killing of Women and Girls (femicide/ feminicide)
  • Module 6: Technology-facilitated Violence Against Women

{%} {%: Audience}

The e-learning course is designed for professionals working in the field of crime and criminal justice statistics, whose primary responsibilities include the collection, production, analysis, and dissemination of crime-related statistics and indicators. The course is also open to policymakers and decision-makers seeking to better understand the concepts and methodological foundations of crime- and gender-related SDG indicators, and to accurately interpret and apply these data for evidence-based decision-making.

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