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Record W2969065086 · doi:10.3390/su11164485

“We Are Prisoners in Our Own Homes”: Connecting the Environment, Gender-Based Violence and Sexual and Reproductive Health Rights to Sport for Development and Peace in Nicaragua

2019· article· en· W2969065086 on OpenAlexafffund
Lyndsay Hayhurst, Lidieth del Socorro Cruz Centeno

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsPhotovoiceReproductive healthGender studiesEnvironmentalismHuman rightsParticipatory action researchPsychological interventionSexual violenceSociologyGender analysisStructural violencePoliticsPolitical sciencePsychologyCriminologyEconomic growth

Abstract

fetched live from OpenAlex

This paper draws on postcolonial feminist political ecology theory, feminist theories of violence and new materialist approaches to sport and physical cultural studies—combined with literature on the role of non-humans in international development—to unpack the connections between gender-based violence and the environment in sport, gender and development (SGD) programming in Nicaragua. To do this, postcolonial feminist participatory action research (PFPAR), including visual research methods such as photovoice, was used to better understand, and prioritize, young Nicaraguan women’s experiences of the environment and gender-based violence as they participated in an SGD program used to promote environmentalism and improve their sexual and reproductive health rights. To conclude, the importance of accounting for the broader physical environment in social and political forces was underlined as it shapes the lives of those on the receiving end of SGD interventions.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.334
Teacher spread0.305 · 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 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

Citations23
Published2019
Admission routes2
Has abstractyes

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