Moral landscapes and morally meaningful encounters: how interaction ritual connects conversation analysis and cultural sociology
Bibliographic record
Abstract
This article presents a theoretical argument for examining the previously unexamined interface between the strong program in cultural sociology ethnomethodology/conversation analysis (EMCA). While these two approaches have radically different theoretical and empirical commitments, they nonetheless share a common root in Durkheim's sociology, specifically with regard to the centrality of solidarity, ritual, and morality to collective life. Similarly rooted in Durkheim, Goffman's theory of interaction ritual provides an analytic pivot between EMCA and the strong program. The broader theoretical argument is illustrated using data from interviews with adults about their most recent encounter with a rude strangers in public space, which are here treated a breaches of the interaction ritual of civil inattention. Members readily draw on the specifics of a particular stranger interaction gone awry to reflect on the nature of life in public and to expound on their understandings of the ethics of face-to-face interaction and everyday morality more generally. Where EMCA focuses on the discoverability of the organizational features of everyday interaction, the position developed here is concerned with the organization of members' interpretations of everyday interaction. While centered on specific kinds of interactional breaches, by finding common ground between EMCA and cultural sociology, the argument advances a potentially more broadly applicable approach that treats everyday encounters as morally meaningful and everyday lifeworlds as moral landscapes. Developing a comprehensive understanding of copresent interaction as a basic building block of society requires attention to both the organizational dynamics of copresent encounters and to the interpretive resources that ordinary members use to account for and justify their own and others' conduct.
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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.088 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".