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Record W4220657160 · doi:10.1080/24694452.2021.1997567

Global Discourses and Local Disconnects: Gender, Aging, Health, and Well-Being in Uganda

2022· article· en· W4220657160 on OpenAlexaff
Andrea Rishworth, Susan J. Elliott

Bibliographic record

VenueAnnals of the American Association of Geographers · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsPolitical scienceGeographySociologyGender studies

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.382
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the American Association of GeographersSame topicAging, Elder Care, and Social IssuesFrench-language works237,207