MétaCan
Menu
Back to cohort
Record W1971800358 · doi:10.1093/jahist/jar590

Empires, Nations, and Families: A History of the North American West, 1800-1860

2012· article· en· W1971800358 on OpenAlexaboutno aff
Adrienne Caughfield

Bibliographic record

VenueJournal of American History · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicArchaeology and Natural History
Canadian institutionsnot available
Fundersnot available
KeywordsExpansiveTribePoliticsTheme (computing)Face (sociological concept)White (mutation)Government (linguistics)HistorySpanish Civil WarAmerican westEconomic historySociologyPolitical scienceEthnologyLawSocial science

Abstract

fetched live from OpenAlex

Anne F. Hyde’s ambitious work is the second installment in the University of Nebraska Press History of the American West series. Weighing in at 541 pages of text alone, one might think Empires, Nations, and Families would be difficult to absorb. Thankfully, that is not the case.Hyde is a skilled writer whose expansive survey of the West from the Lewis and Clark expedition to the Civil War is both informative and engaging. Hyde discusses trans-Mississippi western history with one unifying theme: The development of the West, she argues, had less to do with national or even international politics than it did the creation of intersocial networks based on economic interests. Fur traders and other early European arrivals recognized their reliance on local tribes to procure the goods they wanted, while Native Americans benefited from guns and other European wares. To cement these trading relationships, white men married Indian women, thereby becoming a part of the extended family of the tribe.Such marriages, and the métis children that resulted, characterized the new face of the region, one that handled the constant shifts in government with equanimity.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.004
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.017
GPT teacher head0.263
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations13
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of American HistorySame topicArchaeology and Natural HistoryFrench-language works237,207