GIJC23 Reporting Tools & Tips
Humanizing without Sensationalizing: Investigating Femicide
How can journalists use data without reducing the murder of women to crime statistics, and produce a narrative that humanizes without sensationalizing?
Tag
violence against women
6 posts
News & Analysis Press Freedom
Covering Iran’s Protests from Afar: Q&A with Radio Zamaneh’s Joris van Duijne
Radio Zamaneh Executive Director Joris van Duijne discusses how his exile media site is reporting from afar on the widespread political protests reverberating across Iran.
News & Analysis Press Freedom
‘Picking Up Pens was a Revolutionary Act’: Telling the Stories of Marginalized, Rural Women in India
Meera Jatav, the co-founder of the award-winning, grassroots feminist media organization Khabar Lahariya, has won admiration for her courageous investigations into gender-based violence and caste in India. Here is the keynote speech she delivered at the Centre for Investigative Journalism’s summer conference in London.
Data Journalism
GIJN’s Data Journalism Top 10: Visualizing Philosophy, El Salvador’s Violence, the UK’s John Bercow, Mexican Bikes, and Russian Data
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from October 28 to November 3 finds The Economist’s summary of a great year in print data journalism, an interesting visualization by Deniz Cem Önduygu of key arguments in Western philosophy, Datajournalism.com’s guide to editing data journalism, and BBC News’ analysis of outgoing House of Commons Speaker John Bercow’s career in numbers.
How a Rural Women’s Paper Became a Muckraking Phenomenon in India
Khabar Lahariya started as a four-page experiment to educate women who were learning to read and write but has now grown into a full-blown newspaper that exposes corruption and society’s injustices. Staffed with women from rural India, the newspaper is shaking a deeply entrenched system of neglect in the small villages of India.
Data Journalism
Data Biographies: Getting to Know Your Data
Too often, inexperienced data users accept the data they receive at face value. Data scientist Heather Krause cautions that data should be treated similar to a human source. Just as you’d do a background check on a human source before publishing what they told you, you need to understand your data.