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Record W3094331890 · doi:10.4324/9780367823498-13

North America: Canada and the United States

2020· book-chapter· en· W3094331890 on OpenAlexaboutno aff
Terry E. Miller, Andrew Shahriari

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyPolitical scienceGenealogyHistory

Abstract

fetched live from OpenAlex

This thirteenth chapter continues the reader’s global journey into the study of world music. The chapter opens with a map of North America that highlights the areas where the text focuses its review. The text then prepares the reader with a brief tour of the sights, sounds, and significant historical events associated with the region before visiting specific locales and their musical and cultural activities. In this chapter, these sites include: Canada, introducing Cape Breton fiddling; and the United States, featuring the Singing School Shape-Note tradition - a common choral performance found in Appalachian churches; Bluegrass, a virtuosic ensemble style that features folk instruments, such as the banjo and mandolin; African-American Gospel Choir, a mainstay of African-American musical identity; Country Blues, a music fundamental to popular music styles emanating from the United States, highlighting Robert Johnson’s performance of “Sweet Home Chicago”; “Nuyorican” Salsa, inspired by the Cuban son tradition discussed in an earlier chapter; and, Native American Powwow, originally associated with Plains Indian communal performance and now characteristic of inter-tribal gatherings. The chapter concludes with Questions to Consider intended to stimulate reflection on the chapter contents, as well as further investigation of related themes.

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)0.001
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 score0.063
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.009
Science and technology studies0.0120.004
Scholarly communication0.0080.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0530.008

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 head0.010
GPT teacher head0.176
Teacher spread0.167 · 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
Published2020
Admission routes1
Has abstractno

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