Global Discourses and Local Disconnects: Gender, Aging, Health, and Well-Being in Uganda
Bibliographic record
Abstract
Women comprise a larger share of the world’s aging population. Because older women occupy two stigmatized statuses, they are deemed a particularly disadvantaged group requiring attention. International organizations claim that the feminization of population aging has the potential to become one of the biggest challenges to gender equality of the twenty-first century due to cumulative impacts of inequalities in later life. Yet, discussions uncritically assume that older women are a permanent minority, ignoring the possibility that the direction of gender inequality or its absence varies. This article engages with these contradictions by examining links between gender, aging, and inequalities. Drawing on human geography perspectives of gender, embodiment, and temporalities, interviews with elderly men and women (n = 53) and key informants (n = 34) in Uganda demonstrate that old age health and well-being is an amalgamation of gendered experiences and social dynamics (re)produced and (re)articulated across the life course. Illuminating how gender inequalities are embodied through diverse spatial and temporal relations exposes a counternarrative to global discourses, revealing that gender and aging are experienced and navigated in sometimes unexpected and contradictory ways. Putting forth a feminist political ecology life course perspective highlights needed geographic attention to antecedent place processes that relationally co-constitute gender – age inequalities.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.011 |
| 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".