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Growing Up with the Country

2018· book· en· W4244810536 on OpenAlexaboutno aff
Kendra Taira Field

Bibliographic record

VenueYale University Press eBooks · 2018
Typebook
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsnot available
Fundersnot available
KeywordsEmancipationKinshipNarrativePower (physics)PoliticsHistorySpanish Civil WarEmigrationGeographyGenealogyPolitical scienceLawArchaeology

Abstract

fetched live from OpenAlex

<italic>Growing Up with the Country</italic> documents the migration of freedom’s first generation out of the South and into the West after the Civil War. A narrative history, the book traces three of the author’s ancestors and their successive migrations in the half-century after emancipation. Between 1865 and 1915, tens of thousands of former slaves sought freedom through a series of experiments in land ownership, town building, and emigration that spanned the Mississippi delta, Arkansas, Kansas, Indian Territory, Texas, West Africa, western Canada, Mexico, and beyond. Deepening and widening the roots of the Great Migration, the book argues that their lives and choices complicate notions of the quintessential domesticity and “biracialism” of the nadir, revealing instead the deeply transnational and multiracial dimensions of freedom’s first generation. The book shows that Indian Territory and early Oklahoma served as one of the first sites of African-American transnational movement in the postemancipation period, decentering the United States in North American history even at the turn of the “American century.” It illustrates the gradual emergence of American “biracialism” and the painstaking construction of race and nation that undergirded the rise of American economic, political, and cultural power at the turn of the twentieth century. Finally, the book reveals that historical erasure of this multiracial, multinational past depended upon the manipulation of family and kinship.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.096
Threshold uncertainty score0.323

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.004
Scholarly communication0.0140.009
Open science0.0010.007
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0960.028

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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 designQualitative
Domainnot available
GenreOther

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

Citations28
Published2018
Admission routes1
Has abstractyes

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