Social Contexts and Building Social Capital for Collective Action: Three Case Studies of Volunteers in the Context of HIV and AIDS in South Africa
Bibliographic record
Abstract
Abstract Social capital is increasingly conceptualised in academic and policy literature as a panacea for a range of health and development issues, particularly in the context of HIV. In this paper, we conceptualise social capital as an umbrella concept capturing processes including networks, norms, trust and relationships that open up opportunities for participation and collective action that allow communities to address issues of common concern. We specifically outline social capital as comprising three distinct forms: bonding, bridging and linking social capital. Rather than presenting original data, we draw on three well‐documented and previously published case studies of health volunteers in South Africa. We explore how social contexts shape the possibility for the emergence and sustainability of social capital. We identify three cross‐cutting contextual factors that are critical barriers to the emergence of social capital: poverty, stigma and the weakness of external organisations' abilities to support small groups. Our three case studies suggest that the assumption that social capital can be generated from the ground upwards is not reasonable. Rather, there needs to be a greater focus on how those charged with supporting small groups—non‐governmental organisations, bureaucracies and development agencies—can work to enable social capital to emerge. Copyright © 2014 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".