The right to lifelong learning: Addressing policy challenges for late-life learning in Canada
Bibliographic record
Abstract
Lifelong learning is essential to support optimum development, cope with life challenges, improve healthy autonomy and contribute to a just, sustainable, and prosperous society. The value of the legal right to lifelong learning is not well understood, tested, or applied, as lifelong learning is rarely extended to all people till the end of life. Education or learning was formally accepted as a human right under the Universal Declaration of Human Rights of 1948. Together with UNESCO Recommendation against Discrimination in Education (1960), these two international agreements ensure access, relevance, and equity of lifelong learning. Possible reasons for low compliance and slow implementation of lifelong learning to the end of life are discussed. Canada’s efforts can serve as a model for lifelong learning policies for later life because, as a federated country, it requires national and provincial laws to work together to achieve the same desired outcome for lifelong learning across thirteen different provinces and territories. Furthermore, for the first time, the 2021 Canadian census provided detailed data for the population aged 65–100 years, and it supports evidence-based policy development regarding for whom, when, what, when, where, and how lifelong learning outcomes can be provided nationally. A combination of need and capacity is a better measure than determining eligibility by age 65–100 years, and the quality of learning should be based on responsiveness to specific needs and its relevance to learners in the last four decades of life. The needs for knowledge range from life management, personal growth, societal contributions, and legacy for the future. Learning options should be continuous, encourage individual choice, and rely on geragogy. To be equitable, learning in later life should be delivered in formal, non-formal, or informal means in residential and institutional settings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".