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Record W2080645114 · doi:10.1002/casp.971

AIDS‐NGOs and political participation: Brazilian and Canadian experiences

2008· article· en· W2080645114 on OpenAlexaffabout
Carlos Roberto de Castro e Silva, W. E. Hewitt, Sharon Sabourin, Sergio Calixto, Elisandra dos Santos, Suzanne Ricard

Bibliographic record

VenueJournal of Community & Applied Social Psychology · 2008
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsWestern University
Fundersnot available
KeywordsPoliticsStigma (botany)Participant observationContext (archaeology)Public relationsSociologyHuman rightsSocial movementPolitical sciencePsychologySocial scienceLaw

Abstract

fetched live from OpenAlex

Abstract Non‐governmental organizations (AIDS‐NGO) are an instrument of political pressure and assistance, often serving as a life reconstruction aid for people living with HIV/AIDS. In this case study, we analyze data from historical documents, in‐depth interviews, and questionnaires obtained from participants and community agents in two AIDS‐NGOs: one in Canada (NGO‐Ca), and another in Brazil (NGO‐Br). Despite contextual differences, both NGOs are involved in a fight against stigma and discrimination that may aggravate existing social exclusion. Variances in political participation are nevertheless evident. In NGO‐Ca, efforts are directed towards maintaining and consolidating human and social rights. In NGO‐Br, the primary goal is building these. In NGO‐Ca, the participant is part of a structured organization where he or she receives the required supports; the NGO is thus a service provider. Conversely, in NGO‐Br, the participant is both the actor and author of collectively constructed supportive actions. It is hoped that the lessons learned from this limited case study will assist in the strengthening of AIDS‐NGO organization and activity, particularly in the developing world context. Copyright © 2008 John Wiley & Sons, Ltd.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.165
GPT teacher head0.504
Teacher spread0.338 · 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

Citations2
Published2008
Admission routes2
Has abstractyes

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