Bibliographic record
Abstract
Education of elderly in Canada is disseminated care of the society at all levels, from local community to the national level. A big consideration is given to all aspects of life in the third age and researches aimed to improve quality of life in this age, are ongoing. Institutional forms of education of elderly include Elderhostel, Life Long Learning Institute and different varieties of clubs and day centers. Education is conceived as a factor of preservation and/or development of different abilities. Programs are adapted to the needs and interests of gray hair students and include all aspects of knowledge and creativity. Knowledge for the sake of knowledge, improving quality of life, but also acquiring qualification, are just some of the factors that motivate seniors for involving in educational programs. Many studies predict future social-demographic trends. Predicted growth of the third age population, their characteristics and needs, are the factors that influence programming and contents of the educational institutions. It is expected that future generations will enter the third age with higher educational level and will have a higher educational needs. Prediction is that educational tourism will be very popular, as well as different forms of physical activities. Process of computer communicating is going to expand. Important fact is that society is ready to except new generations of the third age, respect their needs, and give different possibilities for satisfying their interests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".