11.Ageing Well Public Talks Series 2023-2024 Talk 11 Culture in language learning for older adults. ACCESSIBLE
Bibliographic record
Abstract
10th July2024 - In this talk, Natalia will explore the role of culture in language learning for older adults, highlighting the power of storytelling in recognizing older learners' agency in promoting their well-being. Drawing on her expertise in community-based language learning Natalia will delve into how social stimulation in language classrooms can support healthy ageing and how embracing culture and community, along with social engagement, can help older adults thrive. Dr Natalia Balyasnikova is an assistant professor at York University, Toronto, Canada, with a broad interest in lifelong learning, particularly for older adults. Her current focus is on older immigrants' educational engagement in community-based settings, using creative research methods that merge traditional ethnographic data generation with oral, written, and multimodal storytelling. Through her work, she aims to better understand the complexity of learning processes in later adulthood and suggest new pathways for community-based curriculum and educational policy in the context of changing demographics in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.514 | 0.281 |
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".