MétaCan
Menu
Back to cohort
Record W2516685090 · doi:10.3386/w22591

Migration Responses to Conflict: Evidence from the Border of the American Civil War

2016· preprint· en· W2516685090 on OpenAlexaff
Shari Eli, Laura Salisbury, Allison Shertzer

Bibliographic record

VenueNational Bureau of Economic Research · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsYork UniversityUniversity of Toronto
Fundersnot available
KeywordsSpanish Civil WarPolitical scienceCriminologyGeographyLawSociology

Abstract

fetched live from OpenAlex

The American Civil War fractured communities in border states where families who would eventually support the Union or the Confederacy lived together prior to the conflict.We study the subsequent migration choices of these Civil War veterans and their families using a unique longitudinal dataset covering enlistees from the border state of Kentucky.Nearly half of surviving Kentucky veterans moved to a new county between 1860 and 1880.There was no differential propensity to migrate according to side, but former Union soldiers were more likely to leave counties with greater Confederate sympathy for destinations that supported the North.Confederate veterans were more likely to move to counties that supported the Confederacy, or if they left the state, for the South or far West.We find no evidence of a positive economic return to these relocation decisions.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.105
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.320
GPT teacher head0.538
Teacher spread0.217 · 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 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

Citations6
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueNational Bureau of Economic ResearchSame topicVietnamese History and Culture StudiesFrench-language works237,207