Expanding the African-American Middle Class: Improving Labor Market Outcomes
Bibliographic record
Abstract
The data presented this morning by Doug Besharov show that, despite some progress during the 1990’s, the share of African-Americans joining the middle class in the U.S. has stagnated over the past 20-30 years. At least some of these trends are closely tied to changes in the labor market for Americans with different levels of educational attainment in that time period. What opportunities currently exist for blacks in the labor market, and how do these vary with their level of education? What explains the remaining gaps between whites and blacks, and how might the opportunities for blacks be improved over time? Below I present a brief snapshot of the current labor market for black and white Americans of different education levels. These data reflect longer-term trends in the U.S. economy, and how various groups have adapted to these trends. After considering these data, I review some broad policy options for improving employment and earnings outcomes among blacks. The Data Table 1 below presents data on employment rates and median annual earnings for whites
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".