this15fine.dev

I study what happens to people when institutional systems don't fit them. Sometimes I build better tools for noticing that. The PhD happened because I kept asking questions that the tools I could find didn't quite answer.

Nolan (the gray one) contributes by sitting on whatever I'm currently looking at.

Active Work
active research

what happens after things go sideways

Studying how people respond to academic setbacks — specifically why every measurement tool for resilience is aimed at the anticipation of failure rather than the aftermath. Mixed methods. A lot of student narratives. Occasionally the data says something I wasn't expecting, which is the best outcome.

shipped

local-first NLP pipeline

Thousands of open-response narratives, multi-model NLP stack, metaphor detection, modality shifts, temporal framing. All of it on-machine. Ollama handles inference. DuckDB handles the data. Nobody's cloud is involved.

ollama ate all the RAM.
this is, technically, what I asked for.
currently reading Nora Bateson on warm data. Complex responsive processes. A Luhmann essay that has been open for two weeks.
currently playing Dead Cells. Still. Two hundred hours in. The game remains unimpressed. This is, somehow, not a deterrent.
Research

From what I've read, most frameworks for academic resilience measure whether someone expects to struggle. I haven't found one that measures what happens after.

Grit looks forward. Growth mindset looks forward. Self-worth theory looks forward. I couldn't find an instrument built for the aftermath. That's the gap that caught my attention.

To understand what happens in that gap, I use a mix of approaches — longitudinal GPA trajectories, student narratives about academic difficulty, and institutional data about who gets contacted and who doesn't. The research site is Bobcats Bounce Back (B3) at Texas State University — an outbound intervention that reframes GPA difficulty as one navigable adversity among many, not a deficit.

The theoretical thread that runs through all of it: systems thinking. Every person exists inside layers of context — their classroom, their institution, their family, their culture. Two frameworks can disagree about why a student fails and both be right, because the part worth studying is the interaction.

1
One conversation. That's what the data kept showing.
A single structured contact was associated with roughly doubling the rate at which students returned to good academic standing. Not a semester of intervention. Not ten touchpoints. One conversation. The data was checked several times.
returning to good standing turns out to be more stable than the crisis that preceded it.
that one surprised me too.
Background

I argued against creating the program I now run. On the committee that proposed it, I was the dissenting voice. I didn't think it was doable. When it got approved, I took the job anyway. The failure would be spectacular. The success would prove me wrong. Both seemed worth showing up for.

My undergraduate transcript has 45 credit hours of F's on it. This is relevant context for someone who now studies post-failure academic recovery.

The trajectory: practitioner first, researcher second. 15 years in student affairs — academic advising, retention programs, financial aid, enrollment management. The doctoral program came later, because the questions I was asking needed better tools than professional experience alone could provide.

Environment

Python. R. DuckDB. SQLite. Ollama. Fedora Linux. Podman. Raspberry Pi. Shell scripts held together with comments that say "this should not be necessary."

uptime: unconfirmed. last checked: when it stopped.

the instrument shapes what you can see.
this is not a metaphor. it is also a metaphor.
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