MétaCan
Menu
Back to cohort

Эмиграция молодежи из Таджикистана в страны Организации экономического сотрудничества и развития: история и современные тренды

2023· article· en· W4392253137 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueORIENTAL STUDIES · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationPolitical scienceImmigrationSociological researchDemographic economicsSociologyEconomic growthSocial scienceEconomics

Abstract

fetched live from OpenAlex

Introduction. Being a country with a young age structure, Tajikistan has become a prominent participant of various migration flows in recent years. A large number of labor migrants from Tajikistan to the member countries of the Organization for Economic Cooperation and Development (OECD) have been reported since 2014, which is associated with a fall in the ruble’s exchange rate and a decrease in incomes of migrants to Russia in currency equivalent. At the same time, traditionally Tajik youth used to to study in Russia and Kazakhstan, but in recent decades the flow to OECD member countries has also increased significantly. Goals. The study aims to identify the causes and features underlying the reorientation of the flows of educational migrants from Tajikistan toward new geographical directions, namely the OECD member countries. Materials and methods. The work basically employs two research methods. Firstly, the statistical method processes data on the scope and structure of educational emigration from Tajikistan. Secondly, the sociological method provides insights into outcomes of sociological surveys and expert interviews (secondary analysis of sociological data). The key sources of information are OECD-related data from the OECD.Stat reports, and the author’s survey (conducted online via Facebook social network — banned in Russia — accounts of several associations of Tajik citizens abroad) among young individuals from Tajikistan who study in OECD member countries. The questionnaire contained 17 questions about adaptation and integration of migrants, educational levels of migrants, age-sex structure, migration channels, reasons for the reorientation of labor migrants toward OECD member countries, resettlement, and sectoral employment in host countries. The convenience sampling yielded a total of was 417 individuals who were then undertaking training programs in Austria, Germany, the U.S., and Canada. The survey was primarily seeking to identify adaptation strategies selected by young emigrants from Tajikistan in OECD member countries. Results. So, the article presents the outcomes of the survey. Half of Tajik university graduates try to continue their studies and/or find a job abroad via the Internet. Actually, many tend to view educational migration as an emigration channel. This process is accompanied by that Tajik citizens take additional training or retraining programs, seek to receive acknowledgement certificates for diplomas of Tajikistan, and undergo corresponding courses in the receiving countries. As a rule, they quickly adapt to labor markets in OECD member countries: it takes ‘less than a month’ (or ‘from 1 to 3 months’) to get a job. The working language for most Tajik migrants is English and German, and they get jobs in the fields of education and medicine, which attests to somewhat increased educational levels of theirs.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

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 head0.182
GPT teacher head0.457
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