The Digital Fringe and Social Participation through Interaction Design
Bibliographic record
Abstract
Digital inclusion and its implications for social participation is emerging as a key issue for researchers, designers, educators, industry and communities, as contemporary society shifts from top-down decision-making to a more inclusive process that collaborates with a variety of demographics. Yet, this shift tends to predominantly focus on mainstream communities of highly urbanised settlements, often neglecting segments of society that lack access to resources, digital technology or telecommunications infrastructure. Likewise, people from culturally diverse and marginalised backgrounds, or who are socially excluded, such as people living with disabilities, the elderly, disadvantaged youth and women, people identifying as LGBTQIA, refugees and migrants, Indigenous people and others, are particularly vulnerable to digital under-participation, thereby compounding disadvantage. This special issue presents practical, innovative, and sensitive design solutions to support digital participation for older adults, children with barriers to digital access and urban and regional fringe communities. The intention is to foster digital skills within and across communities, investigate the role of proxies in digital inclusion as an enabler of social interactions, and discuss design strategies and methods for sustaining digital inclusion to eliminate the dilemma of under-participation in the future.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".