Gyventojų senėjimas - iššūkis socialinei ir darbo rinkos politikai
Bibliographic record
Abstract
Different countries, including Lithuania, meet economic and social challenges and changes in the sector of social security. The anticipation of the problems and taking actions for the prevention of their after-effects, which may occur in future, is especially important, taking in to the account the projection of population ageing. \nThis work analysis the challenges for social and labour market policy, arising from the ageing of the population. \nMain purpose of the work is to unfold the point of view of different age groups, women and men, about the ageing process of the population and the challenges of this process for state social and labour market, as well as discovering the opinion of the questioned groups of the ways for solving the problems, caused by the process. Therefore, a survey for the implementation of this purpose has been performed. 100 respondents in the age of 30-69 age have been questioned during this survey. The data of this survey do not reflect the opinion of all Lithuanian inhabitants about the challenges, changes and problems, arising due to the population ageing. This is just a tendency for seeing some particular consistent patterns. \nEssence and reasons of population ageing, world wide and Lithuanian ageing tendencies, state policy on elderly people are being reviewed in this work.\nCurrently have the rates of population ageing due to the reduced birth rate and high emigration level in Lithuania signally quickened. Responding to the challenges... [to full text]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.012 |
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".