Bibliographic record
Abstract
OVER THE PAST TWO YEARS, US citizens have heard a great deal about diversity as it relates to race in general, and African Americans in particular.A string of deaths of unarmed African American men at the hands of white police officers has galvanized the nation's attention.When Michael Brown was shot and killed in Ferguson, Missouri in August, 2014, there was a considerable amount of discussion about the gross underrepresentation of African Americans on the police force and among local politicians.Many observers believed that a racially-homogenous police force and the homogeneity among political leaders partially explained the mistreatment of African Americans at the hands of the white Americans in charge.In the months after Brown's death, more African Americans were killed by police officers.Some of the incidents, including the shooting death of Freddie Gray in Baltimore, were highly publicized.But Gray's death was different-while everyone in charge in Ferguson was white, in Baltimore the state prosecutor, mayor, police chief, and several elected officials were African American.Even the group of six police officers involved in the incident was diverse: Three of the officers charged were black.3
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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.026 | 0.020 |
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".