Potential of the silver economy: employment of over-working-age population in Latvian regions
Bibliographic record
Abstract
The latest forecasts of the European Commission (EC) show that the number of people of working age will drop by 42% by 2070. The aim of the paper is to analyse the socio-demographic structure of the employed population above working age in Latvia, including regional tendencies, to identify variations in the potential of the silver economy. Research methods: scientific publication analysis, analysis of previous research results, time-series analysis using trend analysis, cross-tabulations by gender, by administrative territories, by highest obtained education for employed persons older than 65 (they are already in old-age pensions, but go on with their work), by working a full-time job or part-time job; testing of statistical hypotheses by using chi- square criteria, correlation analysis, data analysis performed with SPSS. According to the Central Statistical Bureau of Latvia, the employability of the population at retirement age (65–74 years) was 24.1% in 2023. In 2023, 17% of males and 13% of females received an old-age pension not exceeding 300 EUR. Since the number of old-age pensions is small, it motivates to keep employment if health conditions are reasonable. Many persons of the post-working age are professionally interested in keeping their employment as they have reached a certain high level of professionalism. The official statistical data indicate that in many cases, a high number of the population above working age receiving solid salaries and wages (more than 6000 EUR/per month) is increasing every year.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".