Online intimacy and well-being in the digital age
Bibliographic record
Abstract
Engagement in intimate social interactions and relationships has an important influence on well-being. However, recent advances in Internet and mobile communication technologies have lead to a major shift in the mode of human social interactions, raising the question of how these technologies are impacting the experience of interpersonal intimacy and its relationship with well-being. Although the study of intimacy in online social interactions is still in its early stages, there is general agreement that a form of online intimacy can be experienced in this context. However, research into the relationship between online intimacy and well-being is critically limited. Our aim is to begin to address this research void by providing an operative perspective on this emerging field. After considering the characteristics of online intimacy, its multimodal components and its caveats, we present an analysis of existing evidence for the potential impact of online intimacy on well-being. We suggest that studies thus far have focused on online social interactions in a general sense, shedding little light on how the level of intimacy in these interactions may affect well-being outcomes. We then consider findings from studies of different components of intimacy in online social interactions, specifically self-disclosure and social support, to indirectly explore the potential contribution of online intimacy to health and well-being. Based on this analysis, we propose future directions for fundamental and practical research in this important new area of investigation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".