MétaCan
Menu
Back to cohort
Record W4389553382 · doi:10.5040/9798400697135

Places of the Underground Railroad

2010· book· en· W4389553382 on OpenAlexaboutno aff
Tom Calarco, Cynthia Vogel, Kathryn Grover, Rae Hallstrom, S. Pope, Melissa Waddy- Thibodeaux

Bibliographic record

Venuenot available
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicAmerican History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsUnderground RailroadPeriod (music)Spanish Civil WarEngineeringCivil engineeringTransport engineeringArchaeologyGeographyHistoryLawPolitical scienceArt

Abstract

fetched live from OpenAlex

This up-to-date compilation details the most significant stops along the Underground Railroad. Places of the Underground Railroad: A Geographical Guide presents an overview of the various sites that comprised this unique road to freedom, with entries chosen to represent all regions of the United States and Canada. Where most works on the Underground Railroad focus on the people involved, this unique guide explores the intricacies of travel that allowed the "conductors" to carry out the tasks entrusted to them. It presents an accurate picture of just where the Underground Railroad was and how it operated, including routes and itineraries and connections between the various Railroad locations. Through information about these locations, the book takes readers from the beginnings of organized aid to fugitive slaves during the period following the American Revolution up to the Civil War. It delineates the possible routes fugitive slaves may have taken by identifying the rivers, canals, and railroads that were sometimes used. And it shows that a network, though decentralized and variable over time and place, truly was established among Underground Railroad participants.

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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.037
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0370.008

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 head0.007
GPT teacher head0.164
Teacher spread0.157 · 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
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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicAmerican History and CultureFrench-language works237,207