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Record W7070333714

An Overview of Development of Gender Based Persecution in Refugee Law under Membership of a Particular Social Group: A Study of Comparative Jurisprudence of Canada, UK & USA

2015· article· en· W7070333714 on OpenAlexaboutno aff

Bibliographic record

VenueJournals & Books Hosting (International Knowledge Sharing Platform) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
FundersIndian Council of Agricultural ResearchHome OfficeUniversity of OxfordBoston College
KeywordsPersecutionRefugeeRefugee lawHomelandFace (sociological concept)PoliticsComprehensive Plan of Action
DOInot available

Abstract

fetched live from OpenAlex

Refugees are the most vulnerable people in the world who flee from homeland for saving life because of well fear of being persecution according to the 1951 Refugee Convention.Reports say that women and child refugee are almost 80% of the total number of refugees.Critics also pointed out that international refugee law is conceptually narrow.It is only limited to particular classes of people that included race, religion, nationality, membership of a particular group of people or political opinion, where women's view, women's persecution was neglected and thus it is difficult for a woman to claim and establish as a refugee.Later, UNHCR introduced several guidelines to overcome the limitation of international refugee law, in particularly for women refugees, who face gender based persecution because of her gender.Further, case laws and guidelines for the women refugees of different jurisprudences also contributed for the protection of women refugees.Now, application for claim of women refugee before the adjudicator is not neglected.Rights for women refugee are well settled at the present world.The aim of this paper is to critically discuss the landmark case laws of Canada, U.K. and U.S. who extremely contributed for the development of gender based refugee claim.And finally, there is a conclusion of the discussion.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.309
GPT teacher head0.434
Teacher spread0.125 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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