Современное состояние г. Текели: aнaлиз положительных и отрицaтельных воздействий
Bibliographic record
Abstract
Study of territories with existing structure of industry and the determination of the direction of post-industrial development of territories is a topical issue for increasing the competitiveness of the Republic of Kazakhstan. They, in turn, will become a basis for the overall development of industry in the country. Compared to other cities with a multifunctional economy, the development of monotowns depends more on changes in the external environment, the state of the economy, the external market conjuncture, etc. This dependence is characterized as a blocking factor of sustainable development in our study. This article describes the current state of Tekelitown. As a result of analysis of key factors influencing the sustainable development of the town, a number of factors limiting the development of the town were revealed. The concentration of natural resources, physical and geographical features of the area are ighlighted as a priority for sustainable development; low level of diversification of the economy, accumulation of industrial waste, environmental threats are considered as a limiting factor in the development of the town. Models of the restoration and support of labor resources in monotowns in the conditions of the crisis are analyzed on the basis of the experience of foreign countries (Canada, USA, Germany, Russian Federation). The results and recommendations will serve as the basis for the economic development of town.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".