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Record W2590284320 · doi:10.1093/jahist/jaw518

Beyond Germs: Native Depopulation in North America

2016· article· en· W2590284320 on OpenAlexaboutno aff
Benjamin Madley

Bibliographic record

VenueJournal of American History · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousMetisPopulationEthnologyHistoryAgency (philosophy)EthnohistoryGeographyGenealogySociologyArchaeologySocial scienceEcologyDemography

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.331
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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