Isolated Circuits: Human Experience and Robot Design for the Future of Loneliness
Bibliographic record
Abstract
The following dissertation considers the role of lived experiences in the technology design process. Using an interdisciplinary human-machine communication framework, it highlights lived experiences of loneliness and the present and future role they may play in the design of social companion robots. Following an interdisciplinary framework requires a critical stance on technology. This dissertation considers the systemic factors contributing to issues of access and disparities in the impact of both loneliness and technology. This work uses a mixed methodological approach of surveys, expert interviews, and interviews with individuals who have lived experiences with loneliness. The literature review examines the areas of loneliness, social companion robotics, and human-machine communication. These fields are considered by their key terms and definitions, pressing challenges, and practical applications. The operational definition of loneliness in this dissertation is an experience of a lack or loss of meaningful connection. Research questions consider the main insights at the intersection between technology, design, communication, and loneliness and the role of technology and users' experiences. Next, user needs and widespread design features are differentiated and determined. Finally, questions discuss the theme of managing user expectations and considering current technological competencies. Four hypotheses come from the literature review and open a discussion of the potential for this technology, the input of users, impacts of prior user experiences with robots, and the perceptions of robots as being suitable for others (such as the elderly or those with disabilities) but not necessarily needed for the participants themselves. Findings from the survey are combined with a phenomenological analysis to suggest that social companion robots can be a facilitating tool for mitigating negative experiences rather than replacing human connection. Emergent findings suggest that loneliness is not a ”bad thing” but rather a necessary part of the human experience that acknowledges a need, similar to hunger. Combining the fields of loneliness, social companion robots, and human-machine communication, the connections between these different fields establish a new perspective on an ancient experience.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".