QDA Map: Descriptor Analysis

QDA Map is a qualitative descriptor analysis of the studies you've included, built from the theory, theme, geography, method, and industry tags Zynthia extracts during AI screening. It's meant to help you spot patterns across your corpus that would be tedious to find by re-reading every paper.

The five descriptor types

• Theory, the theoretical frameworks studies draw on
• Theme, recurring substantive topics
• Geography, where studies were conducted
• Method, research methodology used
• Industry, sector or context studied

Proximity Map

Descriptors are positioned so that ones which frequently co-occur across your included papers sit closer together, letting you see, at a glance, which theories tend to pair with which methods or themes. A "bubble mode" scales each point by how often it appears in your corpus.

Topic Distance

Plots each descriptor by its frequency against its distance from the map's centre, splitting them into core (frequent and central), peripheral, niche, and emerging descriptors. This is a fast way to identify what's foundational to your field versus what's an outlier worth flagging as a gap or an emerging direction.

Descriptor Tree

Every descriptor, grouped by type, with its share of your included corpus, a flat, exhaustive reference list underneath the two visual charts above.

Computing and refreshing the map

QDA Map isn't computed automatically the moment you start a review, it needs a minimum number of included papers with descriptor metadata to produce a meaningful map. Once your corpus is large enough, a "Compute" action builds it; if you keep screening afterward and your included set grows, the map is marked stale and you can recompute it to bring it up to date. Like the other analytics tabs, it respects the all/included/excluded scope filter.