Towards the Design and Evaluation of Interactive Technologies for Social Good
Bibliographic record
Abstract
Computer science permeates our everyday lives in almost every space in the modern fast-paced world. The potential of computer science to address the world’s most complex and immediate problems is unbounded. Digital technologies have connected us to the globe, and yet after coming this far, mere technical knowledge does not seem to suffice a cause that is above the global technological requirement. Creative and interdisciplinary solutions that encompass an understanding of technology and people, along with a deep desire to improve the state of the world is the need of the hour, that is, application of cross-discipline aspects from society and technology towards development of social cause. This report gives a qualitative case study of quantitative surveys that address two of the major social challenges experienced by the society on a global scale, and explore solutions and recommendations with interactive technologies to address them. The report also discusses potential applications of mixed reality, and argues that collaborative mixed reality can be deployed towards achieving the interaction goal between different communication groups. It also suggests and proposes the employment of collaborative mixed reality games as a probable solution to minimize the social barriers encountered. The work draws upon psychology, cultural anthropology, and art and urban studies along with application areas from fields of human-computer interaction, computer supported cooperative work, and ubiquitous computing.
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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 | 0.053 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 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".