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Record W2953506751 · doi:10.22230/cjc.2019v44n2a3331

Antiviral Marketing: The Informationalization of HIV Prevention

2019· article· en· W2953506751 on OpenAlexaffvenueabout
Margaret Macaulay

Bibliographic record

VenueCanadian Journal of Communication · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)AffordanceInnocencePublic relationsAmbivalenceSociologyInternet privacyPolitical scienceMedicinePsychologySocial psychologyComputer scienceVirologyLawHuman–computer interaction

Abstract

fetched live from OpenAlex

Background Leveraging the affordances of technology to enhance human immunodeficiency virus (HIV) prevention efforts has become an increasing public health priority. Grounded in a case study examining the role of networked information technologies in reshaping the HIV prevention landscape for gay men in San Francisco and Vancouver, this article proposes that HIV prevention has become informationalized. Analysis The informationalization of HIV prevention is a convergent and participatory process where networked information technologies not only mediate but also produce HIV risk subjectivities, discourses, and practices in ambivalent ways. Conclusion and implications This article argues that although informationalization creates many important opportunities to revitalize HIV prevention, the binary logic of data and code can unwittingly reproduce hierarchies of guilt/innocence and perpetrator/victim that pose challenges for community-based HIV advocacy efforts.

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)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.022
Scholarly communication0.0110.007
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.030
GPT teacher head0.309
Teacher spread0.279 · 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 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

Citations3
Published2019
Admission routes3
Has abstractyes

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