Bibliographic record
Abstract
Abstract Sociologists have explored the availability and use of the death penalty both in the United States and around the world. The historical trend has been a movement away from the use of this sanction. Today, two thirds of the countries in the world have abolished the death penalty. Furthermore, the United States is the only western democracy that still employs the sanction. Social science research has undoubtedly contributed to the diminishing support for the death penalty. In particular, scholars have exposed the fact that the sanction is both biased and ineffective. Support for the death penalty was, in large part, predicated on the idea that the sanction acted as a deterrent for murder. Despite these views, scores of studies have helped establish the limited deterrent capacity of the death penalty relative to life in prison. Beyond being ineffective, scholarship has also pointed out the racial bias inherent in capital punishment. While studies have come to mixed conclusions about the influence of the defendant's race on the likelihood of receiving the death penalty for murder, there is overwhelming consensus that, in the United States, African Americans who kill white individuals are many times more likely to receive the sanction than are white people who kill African Americans.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.677 | 0.457 |
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".