Mobility and opportunity: Black business owners and inventors cross the US-Canada borders
Bibliographic record
Abstract
In the decades after the Civil War and before the Great Migration, a set of remarkable Black business leaders and inventors emerged, including Elijah McCoy, noted inventor and innovator in engine lubrication; John Sullivan Deas, the first person canning salmon near Vancouver; Rev. Charles Spencer Smith, founder of the Sunday School Union of the AME Church; Georgina Mingo Whetsel, who employed a hundred men in her ice harvesting business; May B. Mason, who made the first Black fortune in the Klondike Gold Rush; William Harvey McCurdy, founder of the Hercules Buggy Company; and Jesse Binga, who founded the first privately owned African American bank in Chicago. Each of these remarkable Black business leaders has received some biographical attention, but they have not been considered together, nor by what linked them: each of these men and women (or, in a few cases, their parents) spent time in both the United States and Canada. Cross-border family mobility affected their business choices, opening opportunities not available to those not on the move. While we do not have enough evidence for a strong causal claim, it is clear this mobility correlates to opportunities which led to unusually successful business careers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".