Modeling Employment on Regional Labor Markets(Through the Example of the Khabarovsk Territory of Russia)
Bibliographic record
Abstract
The disbalance of demand and supply on the labor market acquires special sense for remote and underpopulated settled lands. The lack of perspective estimations of the regional labor markets development leads to the outflow of human capital assets from regions, and their high concentration in central regions of the country. It results in the loss of competition effects on the labor market, aggravation of the mismatch of the demand and supply for labor resources according to the types of economic activity. Ultimately, the inertia policy in the area of planning regional employment can lead to the loss of skilled personnel and loss of effects related to regional specialization. The aim of this article is to substantiate the model to predict the number of the employed in the region. Firstly, the article generalizes regional and national tendencies of the labor market development. Secondly, based on the analysis of demographic and economic characteristics of the region, the model related to predicting the employment in the region is offered. Its quality is proved by subsequent approbation on the basis of real statistic data. The article displays the perspectives of applying such models in other regional economic systems taking into account their industry specialization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".