MétaCan
Menu
Back to cohort
Record W2999181185 · doi:10.32936/pssj.v3i3.118

How Does Mass Immigration Transform the Destination Societies?

2019· article· en· W2999181185 on OpenAlexaboutno aff
Dilshad Sabri Ali

Bibliographic record

VenuePrizren Social Science Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationMass migrationGlobalizationEuropean unionPhenomenonDevelopment economicsThreatened speciesDestinationsPolitical scienceTourismEconomic geographyEconomyPolitical economyGeographyInternational tradeEconomicsLaw

Abstract

fetched live from OpenAlex

The phenomenon of mass migration is explained thoroughly in this paper. It explains how easy global transportation by air and sea in a technological advanced world has made mass migration much easier. Mass Migration has also been made easier by globalization in that borders and boundaries between countries are being eliminated. Mass migration is explained in the sense that it takes into account the immigrants effect on destination countries such as the European Union, the United States of America, and also Canada. It takes into account how destination countries integrate and absorb these migrants within their economic sectors. It also takes into account how global security has been threatened by mass immigration. This paper also explains how national identity is being maintained in destination countries as mass migration influences the culture and beliefs of a country. The content analysis as methodology was used to discover the issue in this article. Key words: Mass Immigration, 11th September, National Identity, Globalization, Content Analysis.

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)0.005
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 score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.009
Scholarly communication0.0080.006
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.286
Teacher spread0.275 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePrizren Social Science JournalSame topicMigration and Labor DynamicsFrench-language works237,207