The use of SMART technologies in censuses: world experience and prospects for Ukraine
Bibliographic record
Abstract
Current records of the population in Ukraine are carried out systematically by the relevant bodies and departments of statistics of various levels. It provides an opportunity to quickly obtain the main statistical characteristics of the population in a short time. However, other important parameters of the country’s population, such as ethnic structure, literacy, education, property status and other important indicators, do not take this into account. They can only be installed by census. The article analyzes the results of censuses of some countries of the world, which decided to introduce innovative technologies - SMART-phones, Internet resources - into this process. The study revealed the advantages and disadvantages of such a census format. In the course of the scientific research, statistical indicators of censuses using innovative technologies, which were conducted in the respective years in the USA, Canada, Lithuania, Estonia, Brazil, Australia were analyzed. The basic normative provisions concerning the census procedure in Ukraine, covered in the main legislative documents, as well as the materials of the Institute of Demography and Social Research named after M. V. Ptukha of the National Academy of Sciences of Ukraine, which develops the program and questionnaire of censuses in our country. SWOT analysis of the use of SMART technologies in the census was conducted to identify the advantages and disadvantages, as well as to outline the prospects and threats of the census using innovative technologies. Benefits include the ability to quickly collect and organize information, low census costs, compared to the traditional option. The main disadvantages of the latest census should be mantioned the need to develop expensive software with a high degree of personal data protection, as well as the complexity of fully transitioning the census to the online platform. Studying the experience of countries that have already conducted population censuses using innovative technologies indicates the importance of legally binding participation in the census questionnaire (either electronically or traditionally). The possibility of conducting two stages of population census in Ukraine in 2020 is indicated: in the online mode and in the traditional format. Such an approach to the census procedure will allow the coverage of respondents in all regions and settlements of Ukraine.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".