Comparative Analysis and Evaluation of the Effectiveness of Demographic Policies in EU Countries (2009-2010)
Bibliographic record
Abstract
PURPOSE: This article contains a comparative analysis and evaluation of the effectiveness of population policies in European Union (EU) countries, using multivariate analysis. DATA AND METHODS: To study these differences, it is primarily necessary to have the relevant data. The most recent database available was developed by the OECD in 2007 and currently covers OECD countries and most EU Member States. We used multivariate analysis to categorize the indicators into the following groups: (a) economic indicators, (b) indicators reconciling work and family life, and (c) demographic indicators. RESULTS: The results of measuring the degree of coherence of factors reveal that the four most important factors influencing the effectiveness of population policy are (i) the average maternal age at first childbirth, (ii) social protection expenditure, (iii) GDP, and (iv) public spending for benefits. Based on the data from the evaluation of the correlation matrix of variables and data, the classification of countries, according to the values of the coefficients of analysis, appears as follows: the Nordic countries (together with France and the United Kingdom), the Southern European countries and the Northern countries: Estonia, Latvia, Lithuania (by a very slight margin Romania), and Bulgaria, Poland, Slovakia (and, marginally, Malta). CONCLUSIONS: The key comparative findings from benchmarking best practices in the context of the European experience are the following: The EU is being demographically transformed as a direct result of an increase in average life expectancy and immigration and a decrease in fertility. Demographic factors are influenced by specific features, in contrast with economic factors which seem be less stable.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".