Forecasting of the Development of Migration Process on the Studied Territory
Bibliographic record
Abstract
The article considers the methods for the analysis of migration processes, their advantages are described. Formulas for calculation of various indicators characterizing the development of migration phenomena are given. A gravity model is constructed to be used to prove the influence of various factors upon immigration flows while studying the problems of migration. The author, referring to statistics, claims that the demographic capacity of Siberia and the Far East is obviously insufficient for development of the natural riches located here and creation of the developed economic and settlement structure. Therefore in this region sharply there is a problem of preservation and fixing of resident population, attraction on a constant residence of socially active migrants. Already now in this region workers of high qualification are especially demanded. It isn't necessary to count on possibility of attraction of enough of the corresponding experts from abroad in the absence of the state system of a selective set. For carrying out effective migratory policy it is necessary to create system of the account and a monitoring of migratory streams, restrictions and regulations of number of foreign citizens. The author of article notes that an unconditional priority there has to be an ensuring the maximum employment of the local population. Border territories where on earnings Chinese go mainly, Koreans and Vietnamese, - the third in Russia in size the center of attraction of foreign labor. Labor migrants arriving to Russia go first of all to construction, the industry and agriculture. Every fifth is occupied in the commerce and trade sphere. As a part of foreign workers persons with an average or a low skill level prevail. From the developed European countries, and also from Japan, Canada and the USA there arrive generally experts, but their share is insignificant.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".