Potential Economic Impact of Increasing Income for Black New Orleanians
Bibliographic record
Abstract
Introduction: Growing inequality in the U.S. was gaining increasing attention well before the COVID-19 shutdown and racial reckoning of 2020. But as recently as the 3rd quarter of 2022, when the national unemployment rate was as low as 3.7 percent and employers were struggling to find workers, the median wages of White full-time workers in the U.S. continue to be 25 percent higher than that of Black full-time workers. According to the most recent data for New Orleans, Black households had median incomes that were 64 percent less than White households in 2021. Given the entrenched nature of racial income disparities across the nation, raising Black income to the same level as White income in Metro New Orleans may seem daunting. While income parity is certainly a worthwhile goal, solving this systemic disparity may therefore benefit from an incremental approach. Thus, as a first step, this report quantifies the economic impact of increasing Black incomes in Metro New Orleans to the level of Black households in comparable Southern metros, and highlights actions that civic leaders can take to both expand the share of Black New Orleanians who are working and increase their income.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".