Beats and throat singing: Sámi DJs tap into growing pride in Indigenous identity

The Guardian - World NewsEN 3 min read 100% complete by Miranda Bryant Nordic correspondentFebruary 18, 2026 at 06:00 AM

Read Original Article

AI Summary

medium article 3 min

Article 3, a Sámi female DJ collective based in northern Norway, is contributing to a growing pride in Indigenous identity through their music. The duo, Alice Marie Jektevik and Petra Laiti, draw inspiration from their Sámi heritage and culture, incorporating traditional elements like joik and throat singing into their DJ sets. Formed in 2018, Article 3 was among the first female Sámi DJ groups, paving the way for other women in the scene and increasing the demand for Indigenous music-focused club nights. They perform at various venues, including outdoor raves and indoor club spaces, and aim to create a safe and inclusive space for people to enjoy Sámi and Indigenous music. They recently performed at the Barents Spektakel art festival in Kirkenes, Norway.

Key Entities & Roles

Key Player Opposition Context

Barents Spektakel 30% Kirkenes 40% European Union 40% Sweden 50% Finland 50% Russia ×250% Norway ×260% Sápmi ×270% Article 3 ×380% Sámi ×1090% Petra Laiti ×490% Alice Marie Jektevik ×390%

Keywords

sámi djs 100% indigenous identity 90% sápmi 80% joik 70% indigenous music 70% article 3 60% sámi culture 50% throat singing 50% female djs 40%

Sentiment Analysis

Very Positive

Score: 0.70

Source Transparency

Source

The Guardian - World News

Classification Confidence

90%

Geographic Perspective

Sápmi

This article was automatically classified using rule-based analysis.

Topic Connections

Explore how the topics in this article connect to other news stories

No topic relationship data available yet. This graph will appear once topic relationships have been computed.

sámi djs indigenous identity sápmi joik indigenous music article 3 sámi culture throat singing female djs

Explore Full Topic Graph

Find Similar Articles

AI-Powered

Discover articles with similar content using semantic similarity analysis.