Bibliographic record
Abstract
This paper considers the discrepancy between 'get tough' approaches to crime, primarily in the U.S. and Canada, and the reality of who is being incarcerated. Reports from multiple jurisdictions indicate that rates of violent crime are declining generally for men and increasing for women, but there is no consensus that the 'get tough' approach is responsible for the decline or increase, and there is little political recognition of the need to address the imbalance in incarceration. The paper focuses on how 'get tough' discourses are perpetuated through selective valuation of 'evidence.' It is found that all evidence is not weighed or weighted equally in policies and practices, especially evidence of the over-representation of certain groups in prison populations. It is recommended that future studies address the larger socio-economic and political contexts and purposes of, and expectations for, incarceration through transdisciplinary work that expands the discussion.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.042 | 0.035 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".