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Record W4390996934 · doi:10.1386/ghhs_00064_2

Mapping the global hip hop nation at 50: Introducing the ‘Hip Hop Atlas’ Special Issue

2022· article· en· W4390996934 on OpenAlexaboutno aff
Sina A. Nitzsche, Greg Schick

Bibliographic record

VenueGlobal Hip Hop Studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
FundersArts and Humanities Research Council
KeywordsBoroughNarrativeHistoryGender studiesMedia studiesSociologyLiteratureArt

Abstract

fetched live from OpenAlex

In 2023, hip hop culture celebrates its 50th anniversary since its founding in The Bronx borough of New York City. The journal Global Hip Hop Studies ( GHHS ) takes this historic date as an occasion to explore the culture’s complex histories, narratives and meanings around the world in its Special ‘Hip Hop Atlas’ Double Issue. Initiated by American hip hop producer Greg Schick and co-edited with German hip hop scholar Sina A. Nitzsche, the double issue, for the first time in the journal’s history, presents sixteen concise histories from all continents of the world including Argentina, Australia, Brazil, Canada, Chile, Czech Republic, Germany, Ghana, India, Ireland, Japan, the Netherlands, Senegal, South Africa, Thailand and Ukraine. The articles explain to larger audiences interested in global hip hop culture when and how hip hop first arrived in a given country and how is has developed since its arrival. How does it combine global with local cultural, linguistic and musical forms to create unique style(s) and modes of expression? What role does it play today in its respective contexts? Providing an analytic overview of the articles written by artists, scholars and educators, the editors argue that after more than 50 years hip hop’s global evolution continues to be a powerful, fascinating and dynamic process which ranges from its existence as an established art form, popular culture and research subject in some world regions to moving towards such a status in others.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.588
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.056
GPT teacher head0.252
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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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