Lives of the Lonely: How Collaborative Consumption Services Can Alleviate Social Isolation
Bibliographic record
Abstract
Loneliness and social isolation are significant public health concerns that affect individual and community wellbeing. Certain urban centers have seen an increase in “lonely deaths” which entail “people, often elderly, dying alone without anyone noticing” (Rashid, 2017). Termed “godoska” by South Koreans, and “kodokushi” by the Japanese (Rashid, 2017), this “death by isolation” (Albinsson et al., 2021) is an extreme consequence of loneliness. Research findings indicate that individuals’ health-related behaviors, their mental and physical health, as well as their risk of death are influenced by the quantity and quality of their social relationships (Umberson and Karas Montez, 2010). According to the Cacioppo Evolutionary Theory of Loneliness, in all age groups, the experience of feeling lonely elicits a host of behavioral and biological processes that contributes to premature death (National Institute on Aging, 2019). Those that are isolated or less socially integrated are physically and psychologically less healthy and thus at greater risk of mortality (Shankar et al., 2011). While this public health concern is being addressed at multiple levels (e.g., government and local community programs), another avenue of exploration is whether sharing economy (SE) initiatives can foster human connections and thereby reduce social isolation and loneliness.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".