An Ounce of Prevention
Bibliographic record
Abstract
The recent social upheaval in the wake of George Floyd’s death is reminiscent of the country’s turmoil in the late 1960s. In response to that social upheaval, President Lyndon Johnson charged what came to be known as the Kerner Commission with investigating the riots’ causes across 20 major American cities and recommending what could be done to prevent their reoccurrence. Among the most prominently mentioned causes of the riots was “inadequate recreation areas and facilities” in impoverished urban areas (National Advisory Commission on Civil Disorders, 1968, p. 104). In its recommendations, however, the Kerner Commission largely ignored the importance of recreation areas and facilities. Our commentary discusses why that was the case then, and why that should not be the case now.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.026 |
| Insufficient payload (model declined to judge) | 0.060 | 0.014 |
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".