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Record W2760883306 · doi:10.7596/taksad.v6i4.1122

Immigrant Rights in Iran and Canada and International Law

2017· article· en· W2760883306 on OpenAlexaboutno aff
Forouzan Lotfi, Sirous Noraei

Bibliographic record

VenueJournal of History Culture and Art Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceTreatyLawImmigrationInternational lawStatuteHuman rightsState (computer science)Immigration law

Abstract

fetched live from OpenAlex

After World War II, migration, particularly in the post-Cold War became a global challenge. Today, there are 191 million migrants around the world that constitutes 3 percent of the world's total population. And it is a fact that has various social, economic, humanitarian, political and especially juridical dimensions and effects at the international level as an international issue. National Immigration Law is a part of the legal system governing the strangers in the host state whose provisions are determined by the domestic legal system of the recent state. Although the standards of international law are intended to govern migration, but in this case, however, the regulation of the source government is ineffective. Unless there are specific treaty arrangements while global recruits in the field of migration are specifically impossible and regional multilateral treaties can only be cited. This article tries to review and analyze the immigrant rights in Iran as a source country and Canada as a host country with their own different rights regarding the immigrants by a descriptive - analytical approach. Because of tangible vacuum in the literature of international law and the need to explore other sources of international law, according to the first paragraph of Article 38 of the Statute of the International Court of Justice, on the one hand and the necessity of this article in Iran as a transit country for migration and particularly to Canada on the other hand, conducting this research is of great importance.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.075
GPT teacher head0.354
Teacher spread0.279 · 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 designNot applicable
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
Published2017
Admission routes1
Has abstractyes

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