<i>Talk 11 - Culture in language learning for older adults – Natalia Balyasnikova - Ageing Well Public Talks Series 23/24</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.<br><i>Dr Natalia Balyasnikova</i><i> 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.</i><br><br>
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.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.135 | 0.002 |
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 it