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Record W78597974

Speaking in Song: Power, Subversion and the Postcolonial Text

2011· article· en· W78597974 on OpenAlexvenueno aff
Helen Nabasuta Mugambi

Bibliographic record

VenueCanadian review of comparative literature · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican studies and sociopolitical issues
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)LiteratureCasualSubversionHistoryArt
DOInot available

Abstract

fetched live from OpenAlex

Because song is one of the most pervasive oral forms in Africa, it is possible that postcolonial writers' frequent recourse to this genre constitutes a mode of re-placement, as referred in the opening quotation. Re-placement is deemed to mean idiomatic relocation signifying (re)placement. This article explores the interface between song and Anglophone postcolonial written texts. My exploration is prompted by the prevalence of song in all genres of African postcolonial texts. Even a casual glance at titles across regions and across generations of African writers will note the pervasiveness of the concept of song in the African writer's agenda. Poets, fiction writers, and playwrights have woven a web of song-conscious texts across the continent. Nigeria's literary tradition offers John Pepper Clark's Song of a Goat (1961), Ojaide Tanure's The 'Endless Song (1989), and Niyi Osundare's song-texts, including Moonsongs (1988), Songs of the Marketplace (1983), and Songs of the Season (1990). Zimbabwe's Sekai Nzenza-Shand offers Songs to an African Sunset (1997) while Ghana's Kofi Anyidoho textualizes song inPraise Song for The Land (200O). The famous songs of Okot p'Bitek (1966-73), Byron Kawadwa's Oluyimba Liva Wankoko [Song of the Cock] (1972), and Okello Oculi's Song for the Sun in Us (2001) are testimony to the enchanted landscape of the song tradition in Uganda. Finally, Kenya's Ngugi wa Thion'go's Mother Sing for Me (1982),

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.019
Scholarly communication0.0090.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.032
GPT teacher head0.325
Teacher spread0.293 · 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
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

Citations4
Published2011
Admission routes1
Has abstractyes

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