Socio-Economic Challenges of Population Aging and Opportunities for Social Entrepreneurship
Bibliographic record
Abstract
The aging of the workforce poses significant challenges for the economy, while social entrepreneurship can become a solution. It creates new opportunities for the elderly, ensures their active participation, and strengthens community ties. As a result of demographic changes, in many countries the potential of the retired workforce is once again becoming an important direction of economic and social policy. A global trend is emerging in which retired workers are re-engaged in the labor market through social entrepreneurship. This approach simultaneously addresses two issues: it ensures the social inclusion of the elderly and mitigates the consequences of labor shortages. Social entrepreneurship is considered an effective tool because it combines social mission with economic sustainability. It creates opportunities through which accumulated experience and knowledge are transformed into new value for the labor market, communities, and younger generations. Thanks to this new global trend, individuals excluded from the labor market become drivers of change, while also helping to alleviate emerging professional shortages. This trend is reflected in various initiatives—from “second careers” promoted by Encore.org in the United States, to Mirthy’s digital platform in the United Kingdom, and Seniorpreneurs programs in Canada and Australia.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".