Bibliographic record
Abstract
A concise history of the Indians said to have sold Manhattan for $24 The Indian sale of Manhattan is one of the world's most cherished legends. Few people know that the Indians who made the fabled sale were Munsees whose ancestral homeland lay between the lower Hudson and upper Delaware river valleys. The story of the Munsee people has long lain unnoticed in broader histories of the Delaware Nation. First Manhattans, a concise and lively distillation of the author's comprehensive The Munsee Indians, resurrects the lost history of this forgotten people, from their earliest contacts with Europeans to their final expulsion just before the American Revolution. Anthropologist Robert S. Grumet rescues from obscurity Mattano, Tackapousha, Mamanuchqua, and other Munsee sachems whose influence on Dutch and British settlers helped shape the course of early American history in the mid-Atlantic heartland. He looks past the legendary sale of Manhattan to show for the first time how Munsee leaders forestalled land-hungry colonists by selling small tracts whose vaguely worded and bounded titles kept courts busy - and settlers out - for more than 150 years. Ravaged by disease, war, and alcohol, the Munsees finally emigrated to reservations in Wisconsin, Oklahoma, and Ontario, where most of their descendants still live today. With the four hundredth anniversary of Hudson's voyage to the river that bears his name, this book shows how Indians and settlers struggled, through land deals and other transactions, to reconcile cultural ideals with political realities. It offers a wide audience access to the most authoritative treatment of the Munsee experience - one that restores this people to their place in history.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".