Classification systems sort people into categories and distribute consequences while staying invisible to the people inside them. This program studies them wherever they run: in psychiatric diagnosis, in AI detection, in special education, in the stance of AI products, in the planning layers of AI systems, and in a state rebuilding itself under fire.
Every piece in the program, newest first. Open a row for the full catalog record: abstract, connections, citation, audio thesis.
The program as a graph: 5 themes, one pattern. Select a node to see its connections, each with the written reason the two pieces touch.
Select any node. Cross-theme connections are the argument: the same classification pattern at three institutional altitudes.
A student submits an essay. An algorithm flags it as AI-generated. The student is investigated and sanctioned. The tool has a known error rate, was never validated for non-native speakers, and was purchased by an administrator who will never see the output.
Two passages about the same topic. One was flagged by an AI detection tool. Tap the one you think was flagged.
The process of photosynthesis is essential for converting light energy into chemical energy. Plants use chlorophyll to absorb sunlight, which drives the reaction that converts carbon dioxide and water into glucose and oxygen.
My grandmother used to say that plants were just slow animals, eating sunlight instead of grain. I never understood photosynthesis until I watched her garden come back after a drought, every leaf tilting toward the window like it was listening.
That tool is classification infrastructure: a system that sorts people into categories and distributes consequences while remaining invisible to those inside it. Geoffrey Bowker and Susan Leigh Star showed that the most powerful classification systems are the ones nobody thinks of as classification at all.
The tools are least accurate for the students with the least institutional power to challenge them.
The tools are deployed. The students are sorted. Nobody is watching. This research program watches.
Four classification decisions. Guess what happens to the person being sorted.
A student whose first language is Yoruba submits a research paper. The AI detector scores it at 68% likely AI-generated.
A child scores 71 on an IQ subtest during special education evaluation. The cutoff for services is 70.
A patient describes persistent sadness after a job loss. The clinician checks 'Major Depressive Disorder' on the intake form.
An LLM agent routes a complex query to a cheap inference path to save cost. The output is technically valid but missing critical nuance.
Bowker & Star, Sorting Things Out (1999). Lampland & Star, Standards and Their Stories (2009). Busch, Standards (2011). Eubanks, Automating Inequality (2018). Benjamin, Race After Technology (2019). Weick on sensemaking. March on institutional foolishness.
STS · Critical Education · Disability Studies · Organizational Theory
Jake Lawrence · MPA, Northern Illinois University. 4.5 years deploying court management systems across 100+ Illinois municipalities. The recurring gap between designed systems and lived institutional practice became the central research question.
Scaling from 25 to 100 institutions with longitudinal policy tracking and student outcome data.
The sorting essays and the tracker are converging into a sustained research agenda on classification infrastructure and educational equity.
Each essay in this program is also an interface. The Invisible Architecture embeds games that teach classification. Stance Design mutates its own text to enact invisible manipulation. Unratified puts the reader in the architect’s chair. The form is evolving: essays that make you feel the argument before you read it.
Inside the system: who classified the query?
At the interface: who designed the stance?
In the institution: who sorted the student?
And in every case: can you see it? Can you change it?
Three levels. One pattern. Until it is visible, it cannot be changed. This is the work that makes it visible.
If this work intersects with yours, I want to hear from you. Pick the thread that fits, or write your own.
You study classification systems in a different domain or institution
You have access to institutional data, policy expertise, or affected communities
You're building public scholarship, interactive arguments, or experiential research
Something on this page made you think
Machine-readable catalog: /research/index.json · Ukrainian edition: /ua/research · Hear the theses: /radio
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