Interactional Anomie? Imaging Social Distance after COVID-19: A Goffmanian Perspective
Bibliographic record
Abstract
Social distance is a central issue in the institutional communication about COVID-19. The expression has often been improperly used as a synonym for physical distance. In this article, I will compare how international agencies have used the concept in their documents with Erving Goffman's sociological theory on social distance. The Canadian sociologist is, in fact, the author who has explored the sociological aspects of social distance most deeply. In the third section, summarising Goffman's work, I will try to define a possible research agenda to be developed in the immediate aftermath of the pandemic. Finally, I will analyse some elements of social change already visible in various parts of the world. The aim is to understand how COVID-19 could transform some social and ritual aspects of interpersonal distance. The main hypothesis is that in the immediate aftermath of this pandemic crisis, we will live in a period of moral inter-reign, in which we will experience a form of interactional anomie. This concept is also aimed at integrating the already rich Goffmanian theory on the interaction order, from a perspective that takes in account both the classic Durkheimian concept of anomie connected to dramatic social change and the Parsonsian theory of double contingency. I still do not know how long the pandemic will last and how many further quarantine periods will occur in the future. This is therefore more an exercise in sociological imagination (Wright Mills, 1959) than a sound, grounded theory.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".