FEATURES AND TRENDS CONTEMPORARY PROCESSES OF POPULATION REPRODUCTION IN TERNOPIL
Bibliographic record
Abstract
The article presents the results of the study of the demographic situation in Ternopil. The sex-age structure of the population and indicators that determine the course of population reproduction processes: fertility, mortality and natural increase are characterized. Research has shown that at the beginning of 2021 in Ternopil there were 223,8 thousand inhabitants (21,5 % of the total population of Ternopil region). Compared to 2001, the population decreased by 3,9 thousand people, or 1,7%. In 2020, for the first time in Ternopil, the number of deaths exceeded the number of newborns – a natural increase, thus, fell below zero and became negative (-1,5). The slight increase in the number of Ternopil residents, which has been observed in the last two years, was solely due to internal migration. One of the reasons for this is the high level of unemployment in rural areas of the region, from where migration to the regional center takes place in order to find employment and improve the quality of life. Over the last ten years, there has been a negative trend towards increasing the demographic burden on the working population of persons of working age and post-working age, and at the beginning of 2021 these figures were, respectively, 246 and 432 persons per 1,000 working population. In recent years, there has been a fairly marked decline in the overall birth rate. In 2020, it was at the level of 9 ‰, which is a quarter (25,6 %) less than in 2014. The total fertility rate at the beginning of 2021 was at the level of 11 children per 10 women, which is not enough for simple reproduction of the population cities; the current level of this indicator in Ternopil provides for the replacement of generations by only 52,2 %. Over the last twenty years, mortality rates in Ternopil, as well as in Ternopil region as a whole, have been constantly changing and until 2005 had a steady upward trend, then the mortality rate stabilized at 8,3-8,6 ‰, and since 2007 The indicator started to gradually decrease (to 7,7 ‰), but since 2011 the growth trend has been observed again. This increase in mortality is still observed. In 2020, this indicator reached the maximum mark for the entire observation period (2001-2020) – 10,6 ‰. It is noteworthy that the death rate in Ternopil has always been lower than in the Ternopil region as a whole. This is due to a number of factors, primarily the fact that in the age structure of the population of Ternopil is much smaller share of the elderly (12,5 %) than in the region as a whole (15,6 %), which have the highest mortality rates. Improving the demographic situation in Ternopil is possible under several conditions: increasing real incomes and overcoming poverty; reducing unemployment and shadow employment, reforming the social assistance system in order to strengthen its targeting of socially vulnerable groups, improving the quality and accessibility of preventive and medical care, stimulating the birth rate, spreading healthy living standards, etc. The complexity of solving the demographic problems that have developed in Ternopil is due to the significant inertia of demographic processes, and therefore the longer their solution is delayed, the larger they will become. Given the current demographic situation in Ternopil, it is important to predict the number and gender and age structure of the population in the future. Therefore, in the medium and long-term forecasting of the qualitative and quantitative composition of the city’s population, it is necessary to identify priority measures to mitigate negative demographic trends, as well as to study the dynamics of the working population, because this age group will be the main labor force. further socio-economic development of Ternopil. Key words: demographic situation, population size, depopulation, population aging, fertility, mortality.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".