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The knife's edge: Masculinities and precarity in East Africa

2020· article· en· W3033748418 on OpenAlexafffund
Danya Fast, David Bukusi, Eileen Moyer

Bibliographic record

VenueSocial Science & Medicine · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of British Columbia
FundersEuropean Research CouncilNational Commission for Science, Technology and InnovationTanzania Commission for Science and TechnologyMichael Smith Health Research BC
KeywordsPrecaritySociologyGender studiesPoliticsEmbodied cognitionPolitical scienceLaw

Abstract

fetched live from OpenAlex

In our field sites and clinical practice in East Africa, we regularly encounter men who have become overwhelmed by "thinking too many thoughts" and "gone crazy from confusion," brought about by the problems of life created by deepening social, economic and political precarity. Across diverse settings, many African men continue to be enmeshed in social and material obligations and expectations that position them as economic consumers and providers for those they care for and love. When these gendered obligations, expectations and fantasies are left unfulfilled, this sense of failure can be embodied to produce particular kinds of health effects. Namely, men may become plagued by troublesome and confusing thoughts, leading them in some cases to "give up on" (as our research subjects put it) pursuing work and education, to become immersed in problematic drug and alcohol use, and even to take their own lives. While these afflictions can be glossed using the language of depression, anxiety, addiction and suicide, such medicalizing frames may obscure more nuanced social, structural and affective diagnoses of what is happening to men across Africa and globally. Anthropology provides us with alternative frames through which to understand how psychological wounds are made-and healed.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.745
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.003
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 head0.073
GPT teacher head0.327
Teacher spread0.254 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations28
Published2020
Admission routes2
Has abstractyes

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