Miscalibrated: A Search for Better Outcomes in Legal Punishment, Pretrial Systems, and Beyond
Bibliographic record
Abstract
The seeds of this project were sown in Harrisburg, Pennsylvania in 2017. Having recently graduated high school, I found myself in a studio apartment. I was enrolled at Harrisburg Area Community College and wanted to devote all my time to school so that I could transfer to a nice university. I had signed a lease and had to come up with rent each month. This resulted in an unfortunate reality: given that I was only a high school graduate, it was nearly impossible for me to command an hourly rate high enough to cover my expenses without working at least 30 hours a week. With 30 hours as the minimum workload and a heavy incentive to work as few hours as possible to maximize my academic performance, my life seemed to be defined by a very thin financial margin each month and all the stress associated with it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.032 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.041 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".