Gendered Emotional Support and Women’s Well-Being in a Low-Income Urban African Setting
Bibliographic record
Abstract
women may be especially important for single mothers because of precarious ties to their children's fathers, the prevalence of extended matrifocal living arrangements, and gendered norms that place men as providers of financial rather than emotional support. However, in contexts marked by economic insecurity, spatial dispersion of families, and changing gender norms and kinship obligations, such an expectation may be problematic. Applying theories of emotional capital and family bargaining processes, we address three questions: 1) what is the gender composition of emotional support that single mothers receive? 2) how does gender composition change over time? and 3) does the gender composition of emotional support affect self-reported stress of single mothers? Drawing on data from a unique dataset on 462 low-income single mothers and their kin from Nairobi, Kenya, we uncover three key findings. One, whereas the bulk of strong emotional support comes from female kin, about 20% of respondents report having male dominant support networks. Two, nearly 30% of respondents report change in the composition of their emotional support over six months favoring men. Three, having a male dominant emotional support network is associated with lower stress. These results challenge what is commonly taken for granted about gender norms and kinship obligations in non-Western contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".