The triple differential vulnerability of female entrepreneurs to climate risk in<scp>sub‐Saharan</scp>Africa: Gendered barriers and enablers to private sector adaptation
Bibliographic record
Abstract
Abstract The ability of businesses to adapt effectively to climate change is highly influenced by the external business enabling environment. Constraints to adaptive capacity are experienced by small and medium enterprises (SMEs) across sub‐Saharan Africa, regardless of the gender of the business owner. However, gender is a critical social cleavage through which differences in adaptive capacity manifest and in Africa most entrepreneurs are women. We conduct a systematic review to synthesize existing knowledge on differential vulnerability of female entrepreneurs in Africa to climate risk, in relation to their sensitivity to extreme climate events and their adaptive capacity. We synthesize this literature using a vulnerability analysis approach that situates vulnerability and adaptive capacity within the context of the wider climate risk framework denoted in the IPCC Fifth Assessment Report. In doing so, we identify gendered barriers and enablers to private sector adaptation and suggest women entrepreneurs face a “triple differential vulnerability” to climate change, wherein they: (1) are often more sensitive to climate risk, as a result of their concentration in certain sectors and types of enterprises (e.g., micro SMEs in the agricultural sector in remote regions); (2) face additional barriers to adaptation in the business environment, including access to finance, technologies, (climate and adaptation) information and supportive policies; and (3) are also often concurrently on the frontline of managing climate risk at household levels. Since various forms of inequality often create compounding experiences of discrimination and vulnerability, we pay particular attention to how factors of differential vulnerability intersect, amplify, and reproduce. This article is categorized under: Climate and Development > Social Justice and the Politics of Development Vulnerability and Adaptation to Climate Change > Institutions for Adaptation
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".