Modeling Employment on Regional Labor Markets(Through the Example of the Khabarovsk Territory of Russia)
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it