Police Homicides: The Terror of “American Exceptionalism”
Bibliographic record
Abstract
Police-on-civilian homicides have become a critical social issue in the US in recent years due to newly emerging information on the parameters of the problem and the often egregiousness of the killings. Key to the heightened attention is the increasingly widespread recording and sharing of these gruesome killings via cell-phone cameras, social media, and police cameras. Particularly disturbing is the virtual impunity from sanctions accorded nearly all such shooters, along with the startling frequency of the shootings in the US, as compared with other advanced societies. Over 1,000 persons have been killed by police annually in the US in recent years, with nearly all shot to death, and the remainder tasered, beaten, or otherwise slain. In the first 24 days of 2015, 59 persons were killed by police in the US, whereas only 55 persons were correspondingly slain in the UK in the last 24 years. Similar imbalances exist compared with other advanced nations (e.g., Germany, Japan, Canada, France, and Denmark) in both absolute and relative numbers. While a proportion of the US shootings may have been justified (e.g., suspects pointed weapons or shot at police), most involved lesser provocations and the vast majority could have been avoided if the developing de-escalation techniques had been employed. Although more Whites than Blacks are slain by police, the role of racism in the killings of many Blacks is evident from their substantially disproportionate numbers, and from the comparatively trivial nature of their provocations. Overall, Blacks are between two and three times as likely to be killed than Whites.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".