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Assisting and Protecting Refugee Women: A Policy Analysis

2009· article· en· W2492114473 on OpenAlexaboutno aff
Barbara J. Kampa, Raphael Nawrotzki

Bibliographic record

VenueThe International Journal of Interdisciplinary Social Sciences Annual Review · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Human Rights and Reproductive Law
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeePolitical scienceComputer securityBusinessComputer scienceLaw

Abstract

fetched live from OpenAlex

The number of refugees and internally displaced persons (IDPs) has risen sharply over the last decade. This trend is the result of several causes such as the impact of climatic change, conflicts over diminishing resources, and religious and ethical disagreements. The largest and most vulnerable subgroup among refugees is women and their dependent children, and they are frequently subject to abuse and neglect. To address protection issues, the United Nations High Commissioner for Refugees (UNHCR) released the Policy on Refugee Women in 1990. The authors provide a comprehensive policy analysis, building on an exploration of the historical background and a presentation of policy goals. This exploration sets the stage for a discussion of the influence and viewpoints of major interest groups, such as donors, governments, and non-governmental organizations. The authors draw upon casestudies and a variety of literary resources to explore diversity issues, social justice concerns, and ethical interests. Furthermore, the authors assess the policy's implementation success by using the categories of positive outcomes (institutional change, new programming tools, improvement in refugee situation) and unintended outcomes (cultural and religious opposition, one-sidedness, negative conception). Finally, the authors present a comparison of the applications and implications of the 1990 UNHCR Policy from a global perspective, focusing primarily on the United States, United Kingdom, and Canada as exemplary countries. The paper concludes with a set of recommendations for policymakers and project managers to further improve protection and assistance programs to meet the needs of refugee women and girls worldwide. © Common Ground, Barbara J. Kampa, Raphael Nawrotzki, All Rights Reserved.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.733
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.032
GPT teacher head0.432
Teacher spread0.400 · 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 designTheoretical or conceptual
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
Published2009
Admission routes1
Has abstractyes

Explore more

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