Bibliographic record
Abstract
At a time when loneliness emerges as a major social issue in the post-pandemic world, this article aims to introduce the emerging field of the sociology of loneliness. We argue that, while loneliness studies have traditionally been dominated by psychology, the sociological perspective is increasingly recognized as crucial for understanding contemporary forms of loneliness. Over the past two decades, whether through qualitative, ethnographic, or statistical methods, a growing number of sociological studies have revealed new facets of loneliness across the lifespan. Drawing on an extensive review of this literature, our aim is to identify the specific contributions and distinct features of the sociological approach to loneliness. We show that, compared to other perspectives, this approach fundamentally invites us to consider loneliness not just as an intimate phenomenon, but above all as a social phenomenon, whose causes and consequences are primarily played out at the social and political levels. It therefore reveals structural dimensions of loneliness that are less emphasized in other approaches: the weight of social norms, its social factors and manifestations, the role of inequalities, and its multiple subjective forms. Finally, the article explores the field's main contemporary research horizons and outlines key directions for its further development, highlighting potential intersections with other social sciences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".