DIGITAL NARRATIVE GERONTOLOGY AS BRIDGES BETWEEN GENERATIONS TO IMPROVE WELL-BEING, LEARNING, AND SHARED EXPERIENCES
Bibliographic record
Abstract
Abstract Elders are an essential society component for the collective success of the demographic transition, for social inclusion and the fight against ageism (Berrut, 2020). Ageism - discrimination by age, is a very present concern. One way to respond to this challenge is to build bridges between generations in society; intergenerational programs can reduce the perception of stereotypes about other generations (Barbosa et al., 2021; Pentecouteau & Eneau, 2017), strengthen intergenerational solidarity, and help develop capital and social cohesion (Topping, 2020). Based on the theories of lifelong learning and intergenerational learning, our research engages digital narrative gerontology (Crettenand Pecorini, 2019). We have organized several meetings in pairs (elder – young) where each pair created a digital narrative from the oral narration of the elder life story, supported by the multimedia skills that the young adult acquired during the workshop we have set up. From individual and pairs semi-directed interviews, logbooks, debriefings, and digital narrative, based on case study thematic analysis, our preliminary results confirm the results obtained in 2019: improved general well-being, reduction in the perception of generation stereotypes, reduction in feelings of loneliness and isolation, strengthening intergenerational solidarity, satisfaction to rediscover one’s life or the projection of one’s life stimulated by example, new knowledge acquisition, as well as the pride of artifact creation and its sharing. This innovative project can be applied in schools, colleges, and universities as well as in community centers and seniors’ residences.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".