Estimating <scp>Long‐Run</scp> Incarceration Rates for Australia, Canada, England and Wales, New Zealand, and the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Compiling data from dozens of archival sources, I assemble the most extensive series to date of the long‐run imprisonment rate for five English‐speaking nations: Australia, Canada, England and Wales, New Zealand, and the United States. These series are constructed as a share of adults rather than the entire population, and I discuss why the latter can be misleading. In the late‐nineteenth century, Australia had the highest incarceration rate of these nations. Today, the United States has the highest rate. With the exception of Canada, incarceration rates have risen markedly since the mid‐1980s. These new series are made available in full, to allow other researchers to explore the consequences and causes of incarceration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it