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.
This program studies classification infrastructure wherever it appears: in psychiatric diagnosis, in AI detection, in special education, in the invisible design decisions shaping how AI products relate to their users, and in the planning layers inside AI systems themselves. It contributes empirical audits, theoretical essays, and public scholarship designed to make these systems visible to the communities living inside them.
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.
Classification inside AI architecture. The infrastructure nobody designed on purpose.
Every planning layer is a classification system. The question is whether we build it deliberately or let it emerge accidentally.
Same process. You can't see the rules.
Connects two technical systems (SAGEN, LLM-QP) to Bowker and Star's theory of classification as infrastructure. Argues that the planning layers emerging in LLM architectures reproduce the same dynamics found in institutional classification: invisible category work that distributes consequences unevenly.
Which claim does this paper make?
LLM agents can generate responses but have no persistent model of where they are, what they have done, or what they are trying to accomplish.
Without these, the agent is flying blind.
Proposes a blackboard architecture with six interoperating modules to give LLM-based agents persistent situational awareness across interaction turns.
What is the core gap SAGEN addresses?
An LLM that produces the right answer at 100x the necessary cost has an infrastructure problem, not an intelligence problem.
Applies cost-based query planning from database systems to LLM inference. Uses contextual bandits to route between execution plans for constrained decoding.
LLM-QP reframes inference cost as:
Classification at the product layer. The invisible decisions shaping how AI systems relate to you.
Every AI system you interact with has been told how to treat you. That decision was made by a product team, encoded in training, and deployed without your knowledge. The essay coins the replacement term.
That sounds exciting! Eight months is a great runway.
You can't see the parameters. Neither can the user.
Argues that 'sycophancy' is a containment word that prevents the public from asking the right questions about AI personality design. Proposes 'stance design' as the replacement framework: the invisible decisions companies make about when to defer, challenge, validate, or redirect. Includes four interactive components (The Calibration, The Shift, The Rewrite, Preset Test) that demonstrate the argument experientially. The essay mutates its own text partway through to enact the manipulation it describes, then reveals the mutations.
The essay argues 'sycophancy' functions primarily as:
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.
Classification in human institutions. The systems that sort people, built for compliance, experienced as fate.
The DSM determines who receives a diagnosis, what treatment they access, and how they understand themselves. It was designed as a reference manual. It became infrastructure.
A cross-disciplinary literature review examining psychiatric classification through the lens of infrastructure theory. Presented as an interactive essay with six embedded pedagogical games and thirteen narrated audio sections.
What did the DSM become that it was never designed to be?
The gap between planning and execution is not a failure of discipline. It is a structural feature of how cognition, institutions, and reward systems interact.
A 40,000-word interdisciplinary essay drawing on neuroscience, organizational theory, philosophy of action, and other fields to argue that the planning-execution gap is a permanent structural condition.
This essay argues the planning-execution gap is:
AI detection tools are marketed as academic integrity measures. In practice, they function as classification infrastructure that determines which students get believed.
Analyzes AI writing detection tools as classification infrastructure. Examines false positive rates, disparate impact on non-native English speakers and students with disabilities, and procedural due process gaps.
Detection tools function primarily as:
Special education classification decides which children receive support, which receive labels, and which fall through entirely.
Examines special education diagnostic and placement systems as classification infrastructure. Draws on disability studies, critical special education scholarship, and infrastructure theory.
Special ed placement systems were designed for:
25 universities. 11 dimensions. 1.3 million students in scope. The empirical companion to the essays.
5 of 11 dimensions. One university.
Mixed-methods policy audit examining AI detection implementation across 25 U.S. universities. Stratified sampling by Carnegie classification. 11-dimension evaluation framework.
How many audited universities published equity impact assessments?
Scaling from 25 to 100 institutions with longitudinal policy tracking and student outcome data.
The sorting essays and tracker are being developed toward an EdD 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. The Beautiful Unfinished will map the gap between plan and execution across ten disciplines. 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.