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Record W2990855714 · doi:10.14288/1.0385981

Songs of soldiers : decolonizing political memory through poetry and song

2019· article· en· W2990855714 on OpenAlexaff
Juliane Okot Bitek

Bibliographic record

VenueOpen Collections · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicCaribbean and African Literature and Culture
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPoetryPoliticsHistoryLiteratureArtPolitical scienceLaw

Abstract

fetched live from OpenAlex

In January 1979, a ship ferrying armed Ugandan exiles and members of the Tanzanian army sank on Lake Victoria. Up to three hundred people are believed to have died on that ship, at least one hundred and eleven of them Ugandan. There is no commemoration or social memory of the account. This event is uncanny, incomplete and yet is an insistent memory of the 1978-79 Liberation war, during which the ship sank. From interviews with Ugandan war veterans, and in the tradition of the Luo-speaking Acholi people of Uganda, I present wer, song or poetry, an already existing form of resistance and reclamation, as a decolonizing project. Drawing from political memory in postcolonial, African, Black, Indigenous and Diaspora studies, I argue that truth-telling, a fundamental aspect of reconciliation and restoration of justice among the Acholi, can be achieved through poetic expression. This dissertation extends the technical definition of Okot p’Bitek’s Song school of poetry to include form and content and the space for social and political commentary in various voices and landscapes. The poet as historian, and the artist as ruler, both Okot p’Bitek’s concepts, are illustrated through “Songs of Soldiers”. This work is deeply rooted in displacement and the desire to return – continuing factors in where and how I think about and articulate myself.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.606
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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
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

Citations1
Published2019
Admission routes1
Has abstractyes

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