ПЕРЕПИС НАСЕЛЕННЯ ЯК УНІКАЛЬНИЙ ІНСТРУМЕНТ ІНФОРМАЦІЙНОГО ЗАБЕЗПЕЧЕННЯ УПРАВЛІНСЬКОЇ ДІЯЛЬНОСТІ
Bibliographic record
Abstract
The purpose of the study is to conduct a detailed scientific and practical substantiation of the peculiarities of the population census as a unique tool for information support of management activities. Research methodology. The study used general scientific (generalization, comparison, induction and deduction) and empirical-theoretical methods (analysis, synthesis). The use of system-structural analysis and political science method allowed to differentiate the foreign experience of Canada, USA, Lithuania, Bulgaria, Estonia and Brazil on the use of the latest tools, survey methods, SMART‑technologies in modern censuses. Based on the analysis, it is determined that the use of SMART technologies during the Second All-Ukrainian Population Census in 2023 will have many more advantages than disadvantages. Results. It is established that the census process has the following specific characteristics: each person must be considered separately and their characteristics are registered separately; each person must be registered as close as possible to the same clearly defined time; regular censuses are required to obtain comparative data in a clearly defined sequence. It is determined that the census is used as a reliable source of information support for management, forecasting and management of socio-economic development, for budget implementation, implementation of reforms in the social sphere, regulation of migration processes, demographic forecasting, development of appropriate national development strategy. As a result of the analysis it was established that in different countries the latest tools, survey methods, SMART-technologies were used in conducting modern censuses: survey via the Internet; e-mail survey; survey using a smartphone. Novelty. The scientific novelty of the results obtained in the article is due to the solution of an important scientific problem, which is to develop theoretical principles and practical recommendations for analyzing the characteristics of the census as a unique tool for information management. The article further develops the study on the use of SMART technologies for the census in Ukraine. Practical significance. The results of the study can be used in the study of the scientific field of «Public Administration». The results can be aimed at improving and improving the procedure for the Second All-Ukrainian Population Census in 2023. Key words: population census, management activity, tool, information support.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.016 |
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".