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Record W2536323704 · doi:10.1386/jac.8.2.199_1

Orality, documentary, intertextual performance and discursive practices: A reading of Ye Wonz Maibel (Deluge) 1997 by Salem Mekuria

2016· article· en· W2536323704 on OpenAlexaff
Bunmi Oyinsan

Bibliographic record

VenueJournal of African Cinemas · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture analysis
Canadian institutionsYork University
Fundersnot available
KeywordsObjectivity (philosophy)RhetoricSociologyCitizen journalismOralityLiteratureHistoryAestheticsGender studiesLiteracyArtEpistemologyLinguisticsLawPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Abstract This article offers a reading of Salem Mekuria’s Ye Wonz Maibel (Deluge), a documentary on the Red Terror in Ethiopia under Mengistu Haile Mariam. Mekuria’s film critiques notions of objective and scientific truth on which patriarchal nation states and revolutionary rhetoric often depend. Mekuria does this by using a genre most associated with objectivity and truth – the documentary. Mekuria uses the film as an avenue to get herself and her subjects to actively perform their thinking through of the traumatic events. The process of active introspection allows Mekuria and her subjects to question official accounts of the events. In presenting her subjects’ voices Mekuria challenges the binary victim/oppressor using the notion of the African palaver, and other oral traditions such as sem-enna warq (wax and gold), a major influence in Ethiopian creative expressions. She offers Deluge as a model for participatory intervention and as a discursive and mediational 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 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)0.002
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 score0.008
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0010.002
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 head0.017
GPT teacher head0.294
Teacher spread0.278 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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