A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups
Abstract
When the social boundaries between groups are breached, the tendency for people to erect and maintain symbolic boundaries intensifies. Drawing on extant perspectives on boundary maintenance, we distinguish between two strategies that people pursue in maintaining symbolic boundaries: boundary retention—entrenching themselves in pre-existing symbolic distinctions—and boundary reformation—innovating new forms of symbolic distinction. Traditional approaches to measuring symbolic boundaries—interviews, participant-observation, and self-reports are ill-suited to detecting fine-grained variation in boundary maintenance. To overcome this limitation, we use the tools of computational linguistics and machine learning to develop a novel approach to measuring symbolic boundaries based on interactional language use between group members before and after they encounter one another. We construct measures of boundary retention and reformation using random forest classifiers that quantify group differences based on pre- and post-contact linguistic styles. We demonstrate this method's utility by applying it to a corpus of email communications from a mid-sized financial services firm that acquired and integrated two smaller firms. We find that: (a) the persistence of symbolic boundaries can be detected for up to 18 months after a merger; (b) acquired employees exhibit more boundary reformation and less boundary retention than their counterparts from the acquiring firm; and (c) individuals engage in more boundary retention, but not reformation, when their local work environment is more densely populated by ingroup members. We discuss implications of these findings for the study of culture in a wide range of intergroup contexts and for computational approaches to measuring culture.
Metadata is indexed. Open-access discovery has not completed for this record yet.
No local PDF is available.
GROBID Extracted text; discontinued.
This text is generated from TEI extraction for accessibility, search, and TTS. Formulas, tables, figures, page layout, and references may not perfectly match the original PDF.
No accessible text representation is available. The text extraction service has been discontinued for the time being. If you require this service, for accessibility or any other reason, please submit an issue/request on this page.
Metadata
Issues
No public issues have been filed for this DOI.
Submit an issue
Record history
| When | Event | Field | Old | New |
|---|---|---|---|---|
| 2026-06-18 19:37:53.011249+00:00 | identifier_assigned | DSEID | DSEID-001-8682050 |