Envisioning a McGill University lifelong learning and living (L4) community
Bibliographic record
Abstract
Lifelong learning and cognitive resilience are integral to a changing 21st century education paradigm for learners of all ages, as they are for health and well being of the individual student and wider community. Neuroscience in particular is continually making inroads on the impact that learning has on the brain and the interrelationships between body and mind that help to maintain physical and intellectual capacity over a lifetime. There is a long-standing community dedicated to sustainable lifelong learning on campus, the McGill Community for Lifelong Learning (MCLL). There are also worldwide efforts underway to promote lifelong learning in the context of age friendly cities under the auspices of UNESCO and the World Health Organization. Moreover, the international network of Age Friendly Universities, Lifelong Learning Institutes and over 200 University Based Retirement Communities (UBRCs) in the USA offer resources, guidelines, operating principles and research for building unique and innovative local responses to the changing demographics, increased cultural diversity and technological changes in education futures for learners in a given community. The poster will highlight national and international research initiatives and networks to enhance well-being and mental health through lifelong learning.Different L4 community options will be explored, building on MCLL’s peer learning experience over the past 27 years. It will reflect opportunities for interdisciplinary collaboration with university and wider Montreal community stakeholders, including health care professionals, caregivers, and educators.The poster will demonstrate that a lifelong learning approach to whole person care has the potential to be transformative.
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 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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.007 | 0.045 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".