Bibliographic record
Abstract
The Native American population catastrophe was a formative event in North American history: a cataclysm for indigenous peoples and a central factor in the conquest of the continent. The most widely read explanation of this mass depopulation is surprisingly simple. Popular authors and many scholars assert that Old World germs did most of the killing because Native Americans lacked immunity to new, imported pathogens. Beyond Germs challenges this “virgin soil” hypothesis. Its authors collectively assert that “a variety of causes, in addition to germs, can be shown to have affected indigenous morbidity.” These causes include overwork, destruction of resources and means of production, violence, enslavement, misunderstood or concocted narratives, erasure of indigenous identity, disruption of social nurturing, and the breakup and dispersal of communities. (p. 4) Refreshingly multidisciplinary, Beyond Germs contains ten essays by anthropologists, archaeologists, and historians. They employ “osteological and archaeological data, historic documents, oral records, government policies,” and other sources to address Native American depopulation and survival in multiple regions, “including the Northeast, the Southeast, the Southwest, California, and Mexico” (pp. 3–4). The essays shed new light on North American depopulation while emphasizing both indigenous and nonindigenous human agency in the making of modern North America.
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 | 0.001 | 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 | 0.012 | 0.013 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".