Older people’s production and appropriation of digital videos: an ethnographic study
Bibliographic record
Abstract
While most of today’s children, young people, and adults are both consumers and producers of digital content, very little is known about older people as digital content creators. Drawing on a three-year ethnographic study, this paper reports on the digital video production and appropriation of approximately 200 older people (aged 60–85). They generated 320 videos over the course of the study. We show their motivations for engaging in digital video production, discuss their planned video making, and highlight their creativity while editing videos. We show the different meanings they ascribed to digital videos in their social appropriation of these objects, the meaningful strategies they adopted to share videos, and the impact on their perceived wellbeing. Furthermore, we outline the solutions the participants developed to overcome or cope with interaction issues they faced over time. We argue that the results portray older people as active and creative makers of digital videos with current video capturing, editing, and sharing technologies. We contend that this portrayal both encourages us to re-consider how older people should be seen within human–computer interaction and helps to frame future research/design activities that bridge the grey digital divide.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".