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Record W2996873255 · doi:10.15405/epsbs.2019.12.04.384

Issues Of Population Migration In Practice Of Urban Self-Government Bodies

2019· article· en· W2996873255 on OpenAlexaboutno aff
Irina Shayakhmetova

Bibliographic record

Venue˜The œEuropean Proceedings of Social & Behavioural Sciences · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsOverpopulationPopulationGovernment (linguistics)State (computer science)ResidenceQuarter (Canadian coin)PaceEconomic growthPolitical scienceBusinessGeographyEconomicsSociologyDemographic economicsDemography

Abstract

fetched live from OpenAlex

Migrations of the population in the last quarter of the XIX - early XX centuries were the result of the separation of industry from agriculture, the presence of relative overpopulation, the ruin of the peasants and the emergence of the unemployed. In the regions of arrival of migrants, arrangements were made for their placement. Migrations were both natural and organized by the state. The state in 1932 established a strict passport regime and residence permit. The observance of the passport regime was monitored by the municipal authorities. Prior to the collapse of the USSR, the administrative commissions of the city executive committees resolved issues of the further stay of people in cities. This function of the executive committee of the city council was enshrined in law. The city executive committee was closer to the problems of the population, which allowed its members to clarify the circumstances. Based on this, the decision of the city executive committee was made: to send or leave the violator of the passport regime in the city. The pace of migration from villages to cities accelerated markedly after the lifting of passport restrictions. Every year tens and hundreds of thousands of rural residents rushed to the cities, which resulted in the formation of million-plus cities. Large-scale migrations along with positive consequences had negative ones. They caused the exacerbation of a number of social problems, the solution of which lay with the state, which through central ministries and departments allocated funds distributed to departmental industrial enterprises.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.915

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.295
Teacher spread0.271 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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