The Rise of Loneliness Among Young People
Bibliographic record
Abstract
The article explores the increasing prevalence of loneliness among young people, focusing on digital media’s role in shaping their social experiences. Drawing from historical, psychological, and sociological perspectives, the authors argue that loneliness is not merely physical isolation, but a deeply subjective feeling tied to perceived disconnection from meaningful relationships. Two key hypotheses frame the analysis: the displacement hypothesis (digital media replaces face-to-face interaction and increases loneliness) and the stimulation hypothesis (digital media enhances existing relationships and reduces loneliness). The impact of digital media depends largely on its usage—active versus passive engagement—and the user’s underlying social needs. The article presents findings from a 2022 survey of 654 Slovenian secondary school students. A quarter of respondents reported frequent or constant feelings of loneliness. Students who reported greater loneliness also had more difficulty socializing and were less willing to give up social media use, suggesting a possible dependence on digital interaction as a substitute for real-life connection. The authors emphasize the importance of subjective perceptions of loneliness, arguing that standardized scales often fail to capture this. They advocate for more attention in educational settings to support students experiencing deep loneliness – especially those who may not reach out or appear isolated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".