Celebrity Humanitarianism and the Politics of Development in Sub-Saharan Africa
Bibliographic record
Abstract
Over the past 30 years, celebrities have been increasingly involved in humanitarian and philanthropic work and in bringing attention to poverty, disease and other disasters around the world, especially in developing countries.Africa, in particular, has caught the attention of celebrities, some of whom have turned themselves into advocates for the continent and its development issues.Through their humanitarian and philanthropic work, celebrities have portrayed international development as the panacea for poverty, conflict and disease that plague many countries in sub-Saharan Africa.Their activities, however, raise serious questions about legitimacy, accountability, as well as efficacy and motivation.In light of this, my dissertation engages in a critical theoretical analysis of the phenomena of celebrity humanitarianism and philanthropy by embarking on an empirical research of the work of the Bono-led ONE Campaign and the Bill and Melinda Gates Foundation in South Africa and across sub-Saharan Africa in order to understand how these activities are conducted and how they are viewed by their beneficiaries.My research questions are: what are the benefits and limitations of celebrity humanitarian and philanthropic organizations for development and to what extent have celebrity humanitarianism and philanthropy contributed to development and the improvement of life in South Africa and sub-Saharan Africa?The literature on celebrity humanitarianism and philanthropy has focused on the debate between cosmopolitan/liberal perspectives, which suggest that these practices are a moral good, on the one hand, and critical approaches on the other.Critical perspectives view celebrity humanitarianism and philanthropy as ways of exercising power over, and exploitation of beneficiaries.But few of these critiques are based on empirical research of how celebrity humanitarians and philanthropic foundations operate on the ground and how they are viewed by their beneficiaries.My research addresses this gap.The findings show that although celebrity humanitarian and philanthropic organizations are helpful, there is evident unease with the control that they and their founders have over development in sub-Saharan Africa.Employing a neoliberal imperialism theoretical lens, I argue that celebrity humanitarianism and philanthropy re-entrench the saviour-victim hierarchy, thus perpetuating, rather than challenge, global hierarchies between the Global North and Global South.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".