Bibliographic record
Abstract
An epic deep history of the Indigenous peoples of North America, covering more than 20,000 years of astonishing diversity, adaptation, resilience, and continuity Native America presents an infinitely surprising and fascinating deep history of the continent’s Indigenous peoples. Kenneth Feder, a leading expert on Native American history and archaeology, draws on archaeological, historical, and cultural evidence to tell the ongoing story, more than 20,000 years in the making, of an incredibly resilient and diverse mixture of peoples, revealing how they have ingeniously adapted to the many changing environments of the continent, from the Arctic to the desert Southwest. Richly illustrated, Native America introduces close to a hundred different peoples, each with their own language, economic and social system, and religious beliefs. Here, we meet the Pequot, Tunxis, Iroquois, and Huron of the Northeast; the Navajo, Hopi, Zuni, and Apache of the Southwest; the Hidatsa, Mandan, and Lakota of the Northern Plains; the Haida, Kwakiutl, Nootka, and Salish of the Northwest Coast; the Tule River and Mohave of Southern California; the Cherokee, Creek, and Seminole of the Southeast; and the Inuit and Kalaallit of the Arctic. We learn about hunters of enormous Ice Age beasts; people who raised stone toolmaking to the level of art; a Native American empire ruled by a king and queen, with a huge city at its center and colonies hundreds of miles away; a society that made the desert bloom by designing complex irrigation networks; brilliant architects who built fairy castles in sandstone cliffs; and artists who produced beautiful and moving petroglyphs and pictographs that reflect their deep thinking about history, the sacred, the land, and the sky. Native America is not about peoples of the past, but vibrant, living ones with an epic history of genius and tenacity—a history that everyone should know.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.131 | 0.025 |
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".