Bibliographic record
Abstract
Seniors are amongst the most digitally excluded in Australia. Despite the increasing popularity of social media, seniors often lack access to technology and to basic digital skills. Thus many seniors do not derive the social benefits and service realisation that arise from online forms of communication and engagement. One barrier to digital inclusion for seniors is learning how to make use of digital and online tools in a way that incorporates their specific needs, interests and capabilities. The 60+ Online project fostered digital inclusion amongst 22 Australian seniors with varied digital skills and from diverse socio-economic and cultural backgrounds. Within workshops, researchers encouraged seniors to learn basic digital skills, addressed seniors’ concerns about confidentiality and privacy, and introduced them to safe and regulated online social media platforms. Seniors were encouraged to draw upon personal and community interests to inform storyboarding and digital story development. Digital stories were generated and edited using personal mobile technology. Social media sites (a closed Facebook page and personal Instagram accounts) facilitated sharing of digital skills development and experiences outside the workshops. Regardless of digital skill levels at outset, every senior who completed the workshops ‘graduated’, and produced their own digital story. These digital stories were showcased at festivals, City Council events, and hosted on YouTube. This article outlines the framework used for this project, from the first co-design workshop to YouTube dissemination. We provide links to workshop resources and tools (iPads, smartphones and apps used) in order to provide a model for digital inclusion that may be replicated for other disadvantaged or vulnerable groups in diverse community-based settings.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.895 | 0.848 |
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".