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Record W1512210957

How Do We Increase the Participation of Young Women in hiv/aids Activism? An Interview with Sisonke Msimang

2001· article· en· W1512210957 on OpenAlexvenueno aff
Sonja Klinksy

Bibliographic record

VenueCanadian women's studies · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHIV/AIDS Impact and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsBlameContext (archaeology)Space (punctuation)Youth workWork (physics)Young adultPower (physics)PsychologyPublic relationsSociologyGender studiesSocial psychologyPolitical scienceDevelopmental psychology
DOInot available

Abstract

fetched live from OpenAlex

Sisonke Msimang (SM): One of the biggest challenges is not one that you can blame on young people themselves but one that has to do with the attitudes held by the people who work with youth. Young people are incredibly engaged and involved in HIV/AIDS activism at the service level although in developing countries they are not so active at the policy level. Quite often however the people who work with youth are extremely condescending towards them and limit their decision making power. It is a real challenge to get youth to attend regional meetings as they lack the credentials that more senior participants have. Once they are at these meetings they are often greeted enthusiastically by other members of the community but the actual structure of the meetings are not changed so that they can fully participate. In the African context youth participation also has the added challenge of communication difficulties. There are few networks that allow young people to travel and telephone and email infrastructure is often not as available to youth as it is to other organizations and individuals. In addition the quality of young women’s participation is often determined by the young men who are also participants in discussions and meetings. When young men are present there is often not space left for young women to also participate and speak—-young men do not often realize how much they talk and how much space they can take up. Importantly they often feel that discussing gender issues detracts from discussing youth issues. The attitude is that “we are so marginalized as it is why should we be favouring girls over boys?” So there’s a clear need to educate boys about gender. (excerpt)

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.258
Teacher spread0.214 · 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.

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

Citations0
Published2001
Admission routes1
Has abstractyes

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