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Record W2618791435 · doi:10.1123/ssj.2017-0065

Navigating Norms: Charting Gender-Based Violence Prevention and Sexual Health Rights Through Global-Local Sport for Development and Peace Relations in Nicaragua

2017· article· en· W2618791435 on OpenAlexaff
Lyndsay Hayhurst, Lisa McIntosh Sundstrom, Emma Arksey

Bibliographic record

VenueSociology of Sport Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNorm (philosophy)Gender studiesGender relationsInternational relationsSexual violenceHuman rightsPolitical scienceGender equalityReproductive healthCitizen journalismSexual and reproductive health and rightsParticipatory action researchGirlSociologyPoliticsCriminologyReproductive rightsPsychologyLaw

Abstract

fetched live from OpenAlex

International non-governmental organizations (INGOs) funding sport for development and peace (SDP) programs are drawn to the promise of such initiatives for young women in global South countries such as Nicaragua to promote their sexual and reproductive health rights (SRHR) and prevent gender-based violence (GBV). While “international” feminist norms in support of “girl power” tend to be advocated by INGOs, gender norms in Nicaragua emphasize "machismo’ that tend to uphold male domination. Based on a case study of international-regional-local NGO relations as they “play out” in Nicaragua, this paper connects international relations studies that explore the conditions through which norm change “happens” with postcolonial feminist participatory action research (PFPAR). To conclude, we discuss how to better understand the tensions of "norms in conflict’ in SDP, with a particular focus on the pressures for local NGOs to accommodate—and connect—their contextual circumstances to the demands of transnational partners and the rising focus of Western donor organizations on “measurable” outcomes.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.089
GPT teacher head0.423
Teacher spread0.334 · 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.

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

Citations21
Published2017
Admission routes1
Has abstractyes

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