Digital Intimacy: How Technology Shapes Friendships and Romantic Relationships
Bibliographic record
Abstract
The objective of this study is to investigate the dynamics of digital intimacy, including how individuals use digital platforms to initiate, maintain, and navigate their personal relationships. It seeks to identify the main themes related to digital intimacy, the challenges and benefits associated with it, and the strategies individuals employ to manage their digital relationships. Employing a qualitative research design, this study conducted semi-structured interviews with 28 participants divided into two groups: individuals involved in digital relationships and professionals in the fields of psychology, sociology, and technology. The interviews were analyzed using thematic analysis to identify key themes and categories related to digital intimacy. The study identified seven main themes associated with digital intimacy: Formation of Digital Intimacy, Maintenance of Relationships, Challenges of Digital Intimacy, Benefits of Digital Intimacy, Navigating Digital and Offline Worlds, Evolution of Digital Intimacy, and Characteristics of Digital Intimacy. These themes encompass various aspects of digital relationships, including the initiation and maintenance processes, the role of digital platforms in facilitating emotional connections, and the challenges of privacy, security, and miscommunication. Digital intimacy plays a significant role in shaping modern friendships and romantic relationships, offering both opportunities and challenges. While digital platforms facilitate the formation and maintenance of connections across distances, they also introduce complexities in communication, privacy, and the integration of digital and offline lives. Understanding these dynamics is essential for individuals and professionals working to navigate the digital landscape of personal relationships.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".