The convergence of real and virtual communities in the digital space: a sociological review
Bibliographic record
Abstract
The paper represents a critical analysis of the processes caused by the development of virtual communities, and by the transfer of social practices of traditional communities into the space of interactive communication with subsequent transformations in the nature of interaction and the social roles of its participants. The authors introduce and summarize the approaches to communities such as traditional and virtual, and enunciate the distinctive characteristics of virtual and ‘real’ communities formed on the territory of geographically limited objects (villages, cities, countries), in similar conditions (historical, cultural, linguistic) and existing in a common regulatory and legal field. Based on the assumption that virtual communities are usually geographically disparate, implying significant differences in terms of historical memory, culture, native language, traditions, and other things, the authors prove that they remain communities in the sense that they unite groups of people based on common interests, goals, and views, ensuring the interaction of actors and an information exchange between them. However, the taking on the main distinctive characteristics of traditional communities, virtual communities lose some of the properties traditionally inherent in communities, such as a common territory, history, and culture. Virtual communities are defined by the authors as groups of actors interacting in a virtual space (for example, in a social network) beyond geographic and political boundaries and united by common interests or goals. They are characterized by a significant emotional involvement of the participants in the process of network interaction. In present conditions, when almost any created (and previously created) content is being digitized, real and virtual communities converge in the digital space: thus, (1) virtual communities take on some characteristics of traditional ones, and vice versa; and (2) the likelihood of adding or replacing spatial connections in real communities with virtual communications increases, which creates the convergent communities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".