maat

MA'AT:
Multilingual Archive for Academic Topology
A computational observatory of global higher education discourse


Abstract

MA’AT is a multilingual information system that observes how higher education is discussed across global media, policy documents, and institutional sources. It aggregates RSS feeds from diverse linguistic ecosystems and transforms them into a structured dataset using semantic filtering and language-agnostic embeddings.

The system aims to map recurring patterns in how universities, governance structures, and academic systems are evolving across the world.


System Overview

MA’AT operates as a continuous ingestion pipeline:

Each article is evaluated not by keywords, but by semantic proximity to higher education-related concepts.


Dominant Themes

Category Article Count Lead Article
Higher Education Crisis 199 Il curriculum dello studente e la Maturità-Talent Show di Valditara
University Administration 142 Students, alumni sue to block Kentucky State University overhaul
Teaching And Learning 129 Zwischen Bildung, Kompetenz und Effizienz – Hochschulbildung und ihre versteckten Logiken
Student Experience 75 'Huge relief' as students given loans 'in error' get repayment reprieve
Research Activities 63 Doping accademico e fabbriche di revisioni pilotate
Politics 37 A lawsuit against a Black Lives Matter activist could chill all of our speech
Academic Abuse 35 What UCLA doesn’t want you to know
Education Cost 30 Hotărâre de Guvern privind stabilirea cifrei de școlarizare pentru învățământul preuniversitar și superior (2026 - 2027)
Funding Flows 25 Hotărâre de Guvern privind stabilirea cifrei de școlarizare pentru învățământul preuniversitar și superior
Science Society 23 Tre libri sull’università (e l’orizzonte che non si vede)

Strongest Topic Connections


System Status


Limitations

MA’AT depends on RSS availability and successful article extraction. Paywalled content, incomplete feeds, and extraction failures reduce coverage. Semantic filtering also introduces bias toward conceptually explicit texts, potentially underrepresenting implicit or emergent discourse.


Future Directions