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Record W4210895935 · doi:10.1080/01459740.2022.2027405

Remaking the Technosubject: Kenyan Men Contextualizing HIV Self-Testing Technologies

2022· article· en· W4210895935 on OpenAlexaff
Matthew Thomann, Bernadette Kombo, Helgar Musyoki, Kennedy Masinya, Samuel Kuria, Martin Kyana, Janet Musimbi, Lisa Lazarus, James Blanchard, Parinita Bhattacharjee, Robert Lorway

Bibliographic record

VenueMedical Anthropology · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Manitoba
FundersBill and Melinda Gates Foundation
KeywordsKenyaAgency (philosophy)Government (linguistics)Human immunodeficiency virus (HIV)Value (mathematics)Social psychologyPsychologySociologyMedicinePolitical scienceFamily medicineSocial scienceComputer science

Abstract

fetched live from OpenAlex

The Kenyan government offers free HIV self-testing kits to men who have sex with men. The value of self-testing is based on the imaginary of an autonomous technosubject empowered to independently control testing services, thereby "freed," through technology, from the social conditions that might inhibit health services utilization. Following a community-centered collaborative approach, community researchers interviewed their peers who examined and reacted to the technology. Participants reframed the technosubject as intertwined with the social world and the testing kit itself as an object that exerts agency and possesses affective potential. Attending to these socio-material relationalities offers insights into program planning.

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)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.971
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0290.033
Scholarly communication0.0090.006
Open science0.0010.009
Research integrity0.0030.004
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 head0.032
GPT teacher head0.356
Teacher spread0.324 · 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.

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

Citations6
Published2022
Admission routes1
Has abstractyes

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