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Record W2767208564 · doi:10.1163/18770703-00703002

“Urban Refugees: Fugitive Slaves and Spaces of Informal Freedom in the American South”

2017· article· en· W2767208564 on OpenAlexaboutno aff
Damian Alan Pargas

Bibliographic record

VenueJournal of Early American History · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsnot available
FundersUniversity of CambridgeUniversity of OxfordJohns Hopkins UniversityLouisiana State UniversityHarvard University
KeywordsRefugeePoliticsSociologyProperty (philosophy)LawHistoryPolitical scienceGeography

Abstract

fetched live from OpenAlex

Slave flight in the antebellum South did not always coincide with the political geography of freedom. Indeed, spaces and places within the South attracted the largest number of fugitive slaves, especially southern cities, where runaway slaves attempted to pass for free blacks. Disguising themselves within the slaveholding states rather than risk long-distance flight attempts to formally free territories such as the northern us, Canada, and Mexico, fugitive slaves in southern cities attempted to escape slavery by crafting clandestine lives for themselves in what I am calling “informal” freedom—a freedom that did not exist on paper and had no legal underpinnings, but that existed in practice, in the shadows. This article briefly examines the experiences of fugitive slaves who fled to southern cities in the antebellum period (roughly 1800–1860). It touches upon themes such as the motivations for fleeing to urban areas, the networks that facilitated such flight attempts, and, most importantly, the lot of runaway slaves after arrival in urban areas.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.023
Scholarly communication0.0030.003
Open science0.0000.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.284
Teacher spread0.268 · 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
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

Citations5
Published2017
Admission routes1
Has abstractyes

Explore more

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