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Record W2947626677 · doi:10.15402/esj.v5i2.68350

Learning, Doing and Teaching Together: Reflecting on our Arts Based Approach to Research, Education and Activism with and for Women Living with HIV

2019· article· en· W2947626677 on OpenAlexvenueaboutno aff
Saara Greene, Marvelous Muchenje, Jasmine Cotnam, Peggy Frank, Valerie Nicholson, Apondi J. Odhiambo, Krista Shore, Angela Kaida

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsService-learningStigma (botany)SociologyHuman immunodeficiency virus (HIV)Embodied cognitionGender studiesPublic relationsPsychologyPedagogyPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

Body Mapping has been used for thousands of years by people who want to achieve a better understanding of themselves, their bodies and the world they live in. Artist Jane Solomon and psychologist Jonathan Morgan transformed Body Mapping for the “Long Life Project”, during the Médecins Sans Frontières (MSF) roll-out of antiretrovirals in Khayelitsha township, South Africa in 2001. Body mapping enables participants to tell their stories in the face of intense HIV/AIDS stigma. We adapted Body Mapping for the Women, Art and Criminalizaton of HIV Non-Disclosure (WATCH) study, a community arts based research (CBR) approach to better understand the impact that Canadian laws criminalizing HIV non-disclosure have on women living with HIV. Our national team includes women living with HIV, service providers, and researchers. This reflection illustrates our collective and iterative process of learning, teaching and doing body mapping workshops with women living with HIV in Canada. We share our experiences of coming to Body Mapping as an arts-based approach to CBR, how our roles as researchers stretched to include community-based education, advocacy, and group facilitation, and how we embodied the artist-researcher identity as we disseminate our research in ways that actively engage the general public on laws criminalizing HIV nondisclosure laws vis-à-vis Body Mapping galleries.

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)0.839
metaresearch head score (Gemma)0.541
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.8390.541
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.3100.000
Scholarly communication0.0030.001
Open science0.0010.000
Research integrity0.0000.386
Insufficient payload (model declined to judge)0.0000.000

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 head0.569
GPT teacher head0.617
Teacher spread0.048 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes2
Has abstractyes

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