Digitalization of the EU Economies and People at Risk of Poverty or Social Exclusion
Bibliographic record
Abstract
Despite the fact that a comprehensive analysis of digitalization processes in the EU member states has been carried out, the impact of a country’s digitalization level on the risks of poverty and social exclusion requires further investigation. The purpose of the paper is to verify a hypothesis that a higher level of national digitalization provides positive trends in reducing the risks of poverty and social exclusion for the population. The Digital Economy and Society Index (DESI) was used to evaluate the digitalization levels of the EU countries. The indicator “People at risk of poverty or social exclusion” (AROPE) was applied to estimate the poverty level. As the main research methods, the authors used a comparative and correlation analysis with respect to the above-mentioned indicators, as well as the Monte Carlo method in order to evaluate the probability of a change in the indicator “population at risk of poverty or social exclusion” in 2021. The EU countries with higher digitalization levels have a lower percentage of the population at risk of poverty and social exclusion. However, a higher digitalization level of the EU member states does not provide an accelerated risk reduction of poverty and social exclusion. Statistical calculations with respect to the entire population of these countries mainly indicate reverse processes. At the same time, a further reduction of poverty and social exclusion level is less probable in the countries with a higher level of digitalization. For relatively poor segments of the population (the 1st and 2nd quintiles by income) in the EU member states, the level of digitalization does not play a significant role. For relatively wealthy segments of the population (the 3rd and 4th quintiles by income) the authors noticed a pattern: the higher the level of digitalization is, the lower the risk of poverty and social exclusion becomes. A pairwise comparison of countries with initially similar AROPE values showed that in most cases (3 out of 5), the countries with higher levels of digitalization showed a more significant reduction in poverty and social exclusion. However, the probability of further positive changes in this area is higher for the countries with a lower level of digitalization.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".