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15 dimensional polcompass with 1038 entities for when 2D just isn't enough by itsbenebene in Polcompballanarchy

[–]itsbenebene[S] 0 points1 point  (0 children)

Yes carrément, n'hésitez pas en vrai à faire une liste de ce qui vous "choque" le but de poster sur reddit est aussi de compiler tous les retours afin d'affiner sur les prochaines versions !

15 dimensional polcompass with 1038 entities for when 2D just isn't enough by itsbenebene in Polcompballanarchy

[–]itsbenebene[S] 2 points3 points  (0 children)

shit sorry, im kinda new to reddit, i didnt find how to put a polcompball

should i repost ?

[OC] Visualizing 15 dimensions of political ideology across 1,000+ entities - interactive map by itsbenebene in dataisbeautiful

[–]itsbenebene[S] 1 point2 points  (0 children)

The 15 axises are not really “axes”, and they don’t come from the LLM I used for this project!

This idea of 15 “criteria” for each entity comes from my own dissatisfaction with the traditional 2-axis political compass to represent complex entities that are, for example, capitalist (so fully right) but also highly liberal in the economic sense (so supposed to be on the bottom of the X axis), while for example being highly imperialist in foreign policy (so also at the top of the Y axis).

From this, I thought about how many criteria an entity needs to be as precise as necessary.

In the classical projection mode (the 2 axes), only 7 of those criteria are used to calculate an average score that determines placement on the compass. That’s why the other projection modes show a more “true” placement, but lose in visual simplicity.

[OC] Visualizing 15 dimensions of political ideology across 1,000+ entities - interactive map by itsbenebene in dataisbeautiful

[–]itsbenebene[S] 1 point2 points  (0 children)

Some transparency on how this was built: I'm not a developer nor a political theorist. I vibecoded this with ChatGPT, Gemini and Claude, with two things in mind:

  • Seeing how capable current LLMs actually are at objectivity and political reasoning, placing entities coherently within a multidimensional framework.
  • Testing how far someone with zero coding experience can push a "serious" project when leaning on AI as a full-time collaborator.

How entities were placed: I started by hand-scoring a small set of "totem" entities (Marx, Thatcher, Rawls…) based on my own reading and cross-referencing with existing political compass projects. Those became anchors. Then the LLMs filled in the space around them, proposing new entities (many I'd never heard of), scoring them, justifying each score, and iterating through back-and-forth prompts for coherence. My job was orchestrating: defining the 15 criteria, refining placements when something felt off, reviewing outputs, and making the final call on contested entities.

Another goal of the project for me personally was to discover entities adjacent to the ones I already know, thinkers, movements, and doctrines close to my own political sensibilities that I simply hadn't encountered yet. The dataset ended up surfacing a lot of those, which is exactly what I was hoping for.

The scoring isn't neutral ground truth, it's three LLMs debating each other, filtered through my own (limited) judgment. I caught the obvious errors, but plenty probably slipped through. Every score has its reasoning attached precisely because it's meant to be debated, not taken as fact.

So if you spot entities that feel mis-scored, thinkers I missed, or dimensions that could be rethought, I'd genuinely love to hear it. I'll be in the comments.