Опыт анализа динамики больших временных рядов демографических параметров стран мира и России
Bibliographic record
Abstract
I performed the analysis of large time series of world demographic data and the values of GDP per capita of the countries for long periods of time, starting from 1600 or 1800 years. The data were represented by a non-profit Foundation Gapminder. My results show that these parameters have undergone changes of different size and speed over the time. The strongest and the most variety of changes have occurred with the GDP per capita – from 1800 its value increased in 2787 time in Australia and in 1776 times in Canada. At the same time, in some countries, its value increased 'only' 30 to 40 times (in India and China). Demographic parameters varied more conservative. For 400 years, the number of population in different countries has increased from 5 to 11 times, and the rate of growth, in most cases in the 1600–1800 years grew relatively slowly, the most rapid population growth is happening now, although in a number of developed and very poor countries have declining populations. In general, basic demographic parameters are improved in most countries: life expectancy and net fertility and mortality, infant mortality, maternal mortality. Now in a number of developed countries have been reducing the number of children per woman. In general, over the past 30 years the number of people whose standard of living has reached according to the UN definition, a «moderate degree of development», more than doubled – from 1.6 up to 3.5 billion that is constituted more than a half of the population of the Earth. However, poverty is not terminated and, apparently, will not be terminated for the long time. One of the most difficult questions is the forthcoming growth of the population of the Earth, and I discuss the new approaches to forecast this matter. big time-series; demographic parameters; the gross domestic product per capita; life expectancy; maternal mortality; fertility; forecasting of the population of the Earth
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.030 |
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; both teacher heads agree on what is shown here.
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".