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Human Rights Barriers for Displaced Persons in Southern Sudan

2009· article· en· W2120313551 on OpenAlexaff
Carol Pavlish, Anita Ho

Bibliographic record

VenueJournal of Nursing Scholarship · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Human Rights and Reproductive Law
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHuman rightsFocus groupPolitical sciencePovertyGovernment (linguistics)Public relationsParticipant observationSociologyEconomic growthLawSocial science

Abstract

fetched live from OpenAlex

PURPOSE: This community-based research explores community perspectives on human rights barriers that women encounter in a postconflict setting of southern Sudan. METHODS: An ethnographic design was used to guide data collection in five focus groups with community members and during in-depth interviews with nine key informants. A constant comparison method of data analysis was used. Atlas.ti data management software facilitated the inductive coding and sorting of data. FINDINGS: Participants identified three formal and one set of informal community structures for human rights. Human rights barriers included shifting legal frameworks, doubt about human rights, weak government infrastructure, and poverty. CONCLUSIONS: The evolving government infrastructure cannot currently provide adequate human rights protection, especially for women. The nature of living in poverty without development opportunities includes human rights abuses. Good governance, protection, and human development opportunities were emphasized as priority human rights concerns. Human rights framework could serve as a powerful integrator of health and development work with community-based organizations. CLINICAL RELEVANCE: Results help nurses understand the intersection between health and human rights as well as approaches to advancing rights in a culturally attuned manner.

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)0.002
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: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.051
GPT teacher head0.395
Teacher spread0.343 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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