Are we there yet? Climate philanthropy and the climate action sustainable development goal in Southern Africa
Bibliographic record
Abstract
Climate change is a significant concern in Southern Africa, with increasing reports of severe impacts from weather-related events. Through qualitative methodology, this article evaluates the progress of climate action financing and adoption with insights from 20 key informants from Southern African philanthropic organizations. This is aided through a risk aversion theoretical lens. In depth interviews were conducted between Oct 2022 and March 2023 with representatives from philanthropic organisations in South Africa, Zimbabwe, and Botswana specifically. The study’s findings revealed significant gaps, opportunities, and barriers in climate initiatives, particularly as funding for these efforts remains under 10% of total philanthropic allocations. 80% of the respondents reported that less than 50% allocation of their budget went to climate action. More than half of the respondents reported having less than a 10% allocation of budget to climate activities. Amidst intricate dynamics, compounded by a dearth of regulatory frameworks and policies, along with enduring socio-economic adversities, the disparity between the imperative for climate action and its realization on the ground is starkly evident. The study also found that climate philanthropy is disproportionately driven by funding from the Global North, often motivated by trauma rather than a focus on achieving sustainable development goals.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".