Bibliographic record
Abstract
Abstract Even though the crime rate in the United States has dropped since the U.S. President's Commission on Law Enforcement and Administration of Justice under President Johnson issued its report in 1967, the total number of serious crimes in the nation has increased, and public concern about the subject remains high. The 1960s Commission did not fully consider several major subjects that have emerged after it reported, including mental illness, immigration, cybercrime and other white collar crimes, indigent defense, crime victims, and evidence‐based crime policy. Many observers believe that the need to deal with these subjects in addition to those discussed by other researchers in this volume warrants an examination of crime and justice by a new commission. Congress has considered proposals for such a study for nearly a decade, but they are yet to be acted on amid ideological disputes over other criminal justice issues. If Congress fails to establish a new commission, it is still possible that one could be formed with the support of state, county, and local governments, as well as with the support of private foundations.
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 | 0.039 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.025 | 0.014 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".