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PUTTING PEOPLE LAST: LESSONS FROM THE REGULATION OF MIGRATION IN RUSSIA AND TAJIKISTAN

2016· article· en· W2577394774 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePublic Administration Issues · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsIngredion (Canada)
Fundersnot available
KeywordsDe factoPoliticsPolitical scienceLegislationHuman migrationDevelopment economicsInclusion (mineral)Political economyEconomicsPopulationSociologyLawSocial science

Abstract

fetched live from OpenAlex

This paper analyses the recent developments and general direction of migration policy in two former Soviet Union countries – Russia and Tajikistan – from the lens of the current narrative in the field of migration research, paying attention to the economic and demographic reasons for migration, its legal and political framework, and the nascent integration and inclusion programs. While explaining the roots of the existing migration policy, and distinguishing between the migration policies of sending and receiving countries, the paper defines such terms as migration regime, migration mechanisms and migration regulations. The paper concludes that Russian migration policy reflects the inconsistency between the de jure liberal principles/ norms and their de facto restrictive application. The deeply embedded desire to limit an influx of the “Other” in Russia presents a serious threat to migration policy and the future economic development of the country. By contrast, while developing a comprehensive legislation, Tajikistan lacks the political will and resources to monitor its implementation and progressively demand the delivery of the results.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.949

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.039
GPT teacher head0.332
Teacher spread0.292 · 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