I spent a lot of time laying things out to hopefully clear things up.

The confusion here isn’t personal. It comes from trying to use clean, totalising categories on a reality that is messy, layered, and often internally contradictory. My own framework starts from the assumption that paradox and inconsistency are normal features of the world, not bugs to be eliminated.

When I say “stacking fails,” I’m not saying statistics or population-level analysis are useless. Aggregates obviously exist and are useful at the level they’re meant for. The problem is a different one: stacking people into categories necessarily requires prejudice (Not in a moral sense, but in the sense that you must assume everything about someone based on a limited selection criteria). To reduce prejudice and get things more correct you need to add more attributes to separate people into those categories to make it more accurate, but in so doing the less coherent that category becomes. At sufficiently high resolution, you are no longer describing a group in any meaningful way, you are describing an individual.

That is not a mathematical failure. It is a categorical one. The error happens when you try to take that compressed, simplified image of a population and apply it back onto real, complex human beings.

This is why I am cautious about any framework, including intersectionality, that emphasises the stacking of people into identity variables. If a person is the intersection of dozens or hundreds of factors (history, class, ancestry, education, geography, culture, trauma, opportunity, neurotype, family, etc.), then no small handful of those factors can stand in for the whole person. The more seriously you take that idea, the more it breaks down low-resolution identity thinking. Your piles of people are almost random because even if in aggregate they look a certain way, individually they can be quite different. You can stack into more categories to try to capture people in a more robust model, but as you continue you end up with a near-infinitely complex model, with n piles to represent n people.

This also exposes how unstable many of our categories really are. Ethnicity is not clean or consistent. Language, names, appearance, and culture are shaped by environment, migration, class, and historical accident as much as ancestry. Mexico is a nation, not an ethnicity. A single person can descend from both conquered and conqueror, enslaved and enslaver, oppressed and powerful. These labels collapse under even mild historical or genetic scrutiny.

At the same time, it is obvious that different people experience both advantage and disadvantage, often simultaneously, depending on which variable you examine. The same person can be privileged in one context and marginalised in another. This should make us extremely wary of flattening anyone into a single identity-based narrative.

The real danger appears when systems, not just people, take these simplified categories and operationalise them. Institutions, governments, policies, algorithms, and bureaucracies cannot deal with full human complexity. They are forced to compress people into boxes, risk profiles, types, and groups. Harm happens when those abstractions are mistaken for the actual human beings they are meant to represent.

Governance doesn't necessarily require universal aggregation. That's literally an invention of the modernist era. Before the beginning of the modernist era and the French Revolution, the concept that everyone in a nation needed to be standardized and modularized was not real. One of the reasons for systemic bigotry is that systems were allowed to expand and standardize and make assumptions about everyone living under them, whereas before that governance was more localized. There were problems with that approach as well, but different problems. With a modernist epistemology, this truth is quite invisible, because it's so strongly built into our worldview.

History is full of examples where aggregate observations were converted into essential rules about individuals. In the United States, the history of slavery and race turned a population-level historical condition into a permanent, inherited social status for millions of unrelated individuals. That was not science. It was the misuse of abstraction backed by power.

So I am not rejecting pattern recognition. I am rejecting the move where patterns are treated as people, when a simplified model becomes an identity, and an identity becomes a fate. The aggregate is not the individual. A label is not a human being.