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Record W3184445646

Statistical Footnotes or Suffering Human Beings? Humanising AIDS in Moses Isegawa’s Abyssinian Chronicles.

2021· article· en· W3184445646 on OpenAlexvenueno aff
Edgar Fred Nabutanyi

Bibliographic record

VenuePostcolonial text · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHIV/AIDS Impact and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsForegroundingOpenness to experienceHistorySociologyMilitarizationNarrativeMedia studiesGender studiesPsychologyLiteraturePolitical scienceArtLawSocial psychologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

Uganda is perhaps the only country in the world that has been able to reduce its AIDS infection rates from about 30% in the late 1980s to 6.1% at the turn of the 21st century. This dramatic reversal has been partly attributed to Uganda’s openness about and/or communicative strategies that promoted abstinence, faithfulness and condom use in the fight against AIDS. While a huge corpus of Ugandan medico-anthropological archive that documents this phenomenon privileges statistical data to foreground the impact of the disease on society, another library — consisting of mainly fictional texts — offers an alternative understanding of the disease by foregrounding the corporeality of AIDS through skilful use of characterisation and setting. The fictional representations of AIDS in the Ugandan public sphere, unlike the medico-scientific renditions that essentialise and reduce the disease and its victims to statistical footnotes, offers readers a personalised understanding of the of the disease that unravels its complex contours. In this article, I centre the idea that fiction as an affectively didactic type of knowledge production in AIDS discourses to argue that it is capable of offering profound insights into complex realities like AIDS. Thus, I explore how Moses Isegawa’s Abyssinian Chronicles fuses third person focalisation with spatial and temporal setting to capture the terrifyingly complex and intricate contours of the plague that other documents elide.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.302
Teacher spread0.257 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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