Until we resolve our racially unjust incarceration system, we cannot be at peace
Bibliographic record
Abstract
This year, my home state of Louisiana, United States of America, enacted common-sense prison reforms that will reduce the incarcerated population by 10 per cent and save the state more than a quarter of a billion dollars over the next 10 years. One local sheriff’s response to the reforms encapsulates why they are so desperately needed: he complained that he would be losing a source of free labour. Referring to non-violent offenders, Sheriff Steve Prator of Caddo Parish said, “They’re releasing some good ones that we use every day to wash cars, to change oil in our cars, to cook in the kitchen, to do all that, where we save money.” Video of his offensive statement went viral on social media, with many comparing the prison labour system to slavery.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.019 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".