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Record W4255807287 · doi:10.24908/iqurcp.7806

Gender and Development: Where are the Men?

2017· article· en· W4255807287 on OpenAlexvenueno aff
Corinne L. Mason

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
Fundersnot available
KeywordsMasculinityConstruct (python library)Gender studiesVulnerability (computing)PandemicFocus groupHuman immunodeficiency virus (HIV)Presentation (obstetrics)SociologyPolitical sciencePsychologyGerontologyMedicineCoronavirus disease 2019 (COVID-19)Family medicineDisease

Abstract

fetched live from OpenAlex

This presentation will focus on men, masculinities and the HIV/AIDS pandemic in Southern Africa. As the interest in gender and development in Africa increases among experts in the development field, men have been increasingly left out of the discourse. Given the severity of gender inequality in Sub-Saharan Africa, focus on women is necessary. However, due to the prevalence of issues such as violence and HIV/AIDS prevalence among men, masculinity as a social construct must be given appropriate attention. In 2000, The United Nation Joint Programme on HIV/AIDS launched a World AIDS Day campaign called “AIDS: Men Make a Difference”. A UNAIDS March 2000 report acknowledged the importance of working with men to halt the HIV/AIDS pandemic due to “cultural beliefs and expectations [which] heighten men’s vulnerability” to HIV/AIDS. Similarly, scholars have recently taken interest in the intersection between masculinity and HIV/AIDS. This presentation will provide an overview of the exclusion of men’s issues in development and the reasons why we need to start paying attention to masculinity as a gendered construct.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.011
Scholarly communication0.0100.009
Open science0.0010.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0140.002

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 head0.149
GPT teacher head0.398
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes1
Has abstractyes

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