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Record W2147320208 · doi:10.1080/17290376.2012.744897

Researching to make a difference: Possibilities for social science research in the age of AIDS

2012· article· en· W2147320208 on OpenAlexfundno aff
Naydene de Lange

Bibliographic record

VenueSAHARA-J Journal of Social Aspects of HIV/AIDS · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
FundersNational Research FoundationMcGill UniversityAIDS Healthcare Foundation
KeywordsForegroundingParticipatory action researchAgency (philosophy)SociologyPublic relationsPandemicCitizen journalismPositivismPolitical scienceSocial scienceEngineering ethicsMedicineCoronavirus disease 2019 (COVID-19)Engineering

Abstract

fetched live from OpenAlex

HIV and AIDS is recognized as one of the most devastating pandemics of sub-Saharan Africa, and South Africa in particular, with adverse effect on individuals, families, schools, communities and society at large. Research is therefore required to provide a deeper understanding of the complexities of HIV and AIDS in order to mitigate the effect of the pandemic. Much of the excellent research that has been done has been undertaken within a positivist paradigm and has focused on the biomedical aspects of HIV and AIDS, as well as the social aspects of the pandemic. This theoretical position paper draws on various projects in the field of HIV and AIDS education in rural KwaZulu-Natal to argue that more social science research should be framed within a participatory research paradigm, foregrounding participant engagement and process, and which simultaneously has a "research-as-intervention" focus.Such research adheres to the requirement of knowledge production, but also engages the participants as knowledge producers who, through the research process, are enabled to shift towards taking up their own agency. Reflecting on the findings from the various projects suggests that visual participatory methodologies are particularly useful when working with marginalized persons whose voices are seldom heard especially when exploring topics which are difficult to discuss. Furthermore, it brings issues to the fore and opens up debate, while at the same time democratizing research and allowing universities to take up their social responsibility and to contribute towards making a difference in the communities they serve.

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)0.146
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.146
Threshold uncertainty score0.774

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1460.073
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.004
Science and technology studies0.0200.122
Scholarly communication0.0380.053
Open science0.0030.028
Research integrity0.0170.018
Insufficient payload (model declined to judge)0.0070.001

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 head0.533
GPT teacher head0.629
Teacher spread0.095 · 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 designTheoretical or conceptual
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

Citations14
Published2012
Admission routes1
Has abstractyes

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