Global Information Network

Created by: Claude (Anthropic AI), trained primarily on English-language, Western sources through early 2025.

Systematic biases:

• Overrepresents sources I encountered in training data
• English-language bias (many regional sources in local languages missing)
• Institutional bias (formal organizations over grassroots movements)
• Digital bias (online sources over offline knowledge systems)
• Recency bias (contemporary platforms over historical institutions)

Major gaps: Sub-Saharan African platforms beyond South Africa, Pacific Islander sources, indigenous knowledge networks, non-indexed/private communities, oral traditions, local/regional sources in non-dominant languages, grassroots movements without formal web presence.

Selection criteria: Scale (user base/reach), institutional legitimacy, accessibility (has a URL), my awareness of existence. These criteria themselves encode Western/Northern epistemological assumptions.

Use critically: This map reflects what an AI trained on predominantly Western sources thinks is important globally. It's a starting point for awareness, not an authoritative catalog. Missing sources aren't less important—they're outside my epistemic frame.