"Music co-listening over video chat to support intergenerational connectedness: An exploratory study"
Bibliographic record
Abstract
Background: Meaningful intergenerational interaction can help older adults view aging more positively, provide a means to pass on their cultural identity, and support general well-being.However, maintaining intergenerational relationships may be difficult due to geographical separation, lack of common conversation topics, scheduling challenges, and recently, pandemic-related restrictions.We explored music co-listening over a typical video-conferencing platform to see how such platforms can support a rich and sustained connectedness between grandparents and teen grandchildren.Objective: In this research, we explored the following questions: What interaction and conversation patterns happen when older adults and grandchildren share their music with each other over a synchronous video conferencing tool?What types of intergenerational interactions around music co-listening online should communications technology support, in order to support inter-generational conversation?Method: We conducted a qualitative study where a grandparent and teen grandchild colistened to favourite songs and had a conversation about them.Results: From this exploratory study, we found that the inclusion of music provided a 'Ticket-to-Talk' between our dyads (6 dyads, 12 participants) by supporting peripheral quality interaction with mu-sic.Our 'Private DJ' mechanism simplified the process of colistening to music online and conversing around it for the dyads.The planning of songs to share, anticipating the other party's song selections, watching the partner's song selection, and having time between the songs to have a conversation, all seemed to contribute to making the synchronous intergenerational communication enjoyable between our dyads.Conclusion: Our results support the ongoing design of online family communication technologies to include increased support for co-activities such as music co-listening, to make it easier for separated family members to have meaningful and sustained communications.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".