I've got a home in glory land: a lost tale of the Underground Railroad
Bibliographic record
Abstract
It was the day before Independence Day, 1833. As his bride, Lucie, was about to be sold down the river, Thornton Blackburn planned a daring and successful daylight escape from their Louisville masters. Pursued to Michigan, the couple was captured and sentenced to return to Kentucky in chains. But Detroit's black community rallied to their cause in the Blackburn Riots of 1833, the first racial uprising in the city's history. Thornton and Lucie were spirited across the river to Canada, but their safety proved illusory when Michigan's governor demanded their extradition. Canada's defence of the Blackburns set the tone for all future diplomatic relations with the United States over the thorny issue of the fugitive slave, and confirmed the British colony as the main terminus of the Underground Railroad. The Blackburns settled in Toronto, where they founded the city's first taxi business, but they never forgot the millions who still suffered in slavery. Working with prominent abolitionists, Thornton and Lucie made their home a haven for runaways. When they died in the 1890s with no descendants to pass on their fascinating tale, it was lost to history. Lost, that is, until archaeologists brought the story of Thornton and Lucie Blackburn again to light.
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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 | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".