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Record W2791384587 · doi:10.5539/ells.v8n1p11

Stage or Page? A Dub Performer or A Dub Poet? A Study of Linton Kwesi Johnson’s Political Activism in “Five Nights of Bleeding” and “Di Great Insohreckshan”

2018· article· en· W2791384587 on OpenAlexvenueno aff
Yasser K. R. Aman

Bibliographic record

VenueEnglish Language and Literature Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCaribbean history, culture, and politics
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryPoliticsCreole languagePerforming artsLiteratureIdentity (music)HistoryArtVisual artsMedia studiesSociologyLawAestheticsLinguisticsPhilosophyPolitical science

Abstract

fetched live from OpenAlex

This paper investigates Linton Kwesi Johnson’s political activism in “Five Nights of Bleeding” and “Di Great Insohreckshan” in order to answer the much-debated question: which is more effective in conveying Johnson’s political message: the performed song or the scribed poem? First, the paper gives a brief history of dub music which started in Jamaica, Johnson’s motherland. A discussion of dub poetry follows highlighting the pioneers such as Johnson and Mutabaruka. I argue that the performed songs and the scribed poems under study are effective in convey Johnson’s message each in its own way; however, the scribed form has a stronger, more longstanding impact on imparting the message than stage performance because it relies on the musicality of the words created by sounds and aural images easily grasped even by an international readership alien to the heritage of dub music. An analysis of political events in the two poems shows that a scribed poem, which, as in “Five Nights of Bleeding”, graphically represents a tension between Standard English, and Jamaican Creole and Jamaican English, and which highlights sounds at play as in “Di Great Insohreckshan”, asserting identity, can do without stage performance.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.023
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.328
Teacher spread0.305 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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