From the Underground Railroad to the Promised Land:The American Negro Search for Spiritual Geography
Bibliographic record
Abstract
By way of an introduction, I will begin with three explanatory notes: 1. The subject of this paper is a part of a more general work that deals with various aspects of American Orientalism on which I have been working for over twenty years. The work, parts of which have already been published, is based on the premise that very early in the history of the colonization of North America seeds of Orientalism were sown in the attitude of the immigrants to the concept of the Land of Promise.To give one illustration only, one of the Puritan leaders, John Cotton, wished his fellow Englishmen God's speed on their journey to the New World in an essay he called "God's Promise to His Plantation." Cotton quoted from Scripture: "Moreover I will appoint a place for mv people IsraeL and I will plant them, that they y dwell in a place of their own, and move no ore (2 Sa . 7. 10).1 Early immigr nts sa\v an nalogy between their journey to the New World and the biblical trip of the Israelites from Egypt to the Land of Canaan. The theme has been present in the writings and behavior of Americans down the centuries. I have documented this subject '-elsewhere. 2 7 2. The Underground Railroad is the name that was given to a secret network of agents and safe houses used in mid-nineteenth century by abolitionists to smuggle Negroes to the North or to Canada. .,, 3. The terms 'Negro', 'Blacks', 'African-Americans' are used to refer to the African-American community in the U. S. I will try to be politically correct by using these terms each its in the historical context. .
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".