La gérontologie narrative numérique : porte ouverte sur les apprentissages informels intergénérationnels et les communications numériques
Bibliographic record
Abstract
Ontario’s action plan for seniors recognizes that seniors have health and education needs, among other needs (Gouvernement de l’Ontario, 2017). To partially meet these needs, we have proposed an innovative gerontagogical approach by combining two existing concepts: narrative gerontology with digital storytelling. Thus, by this new concept of Digital Narrative Gerontology, we give seniors a special place for the elaboration of the message they wish to bequeath to other generations, by creating together their life testimonies in digital form easy to share, while mutually acquiring new digital skills and exploiting the benefits of narrative gerontology, such as well-being, aging well, resilience and wisdom, but also informal and intergenerational learning. Communication was important on two levels: between the elder and the researcher during the oral narration and the creation of the digital narration in a relationship of mutual trust as well as in the form of digital testimony with a key message to transmit, during the broadcast of the digital narration. This research also made it possible to respond to the curiosity of the elders for new matters in a reassuring framework, adapted to their level, while respecting their learning pace and the choices of knowledge that they wish to acquire. Seniors were proud to share their digital creation with those they had chosen, thus opening the space for discussions, exchanges, emotions and the open door to a new intergenerational “narrative” and social interactions - sources of learning.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".