Decoding Conflict: Linguistic Markers of Disagreement in Hackathon Design Teams
Bibliographic record
Abstract
Abstract In design teams, conflict can serve as a catalyst for innovation if managed effectively. While prior research has explored the causes and outcomes of conflict, less is known about how language reflects conflict dynamics in real-time. This study examines linguistic markers of conflict in hackathon teams — fast-paced, high-stakes environments where disagreement is frequent. Using deductive coding, we first identify instances of conflict in transcript datasets from two hackathon teams. Then, using sentiment analysis with the Linguistic Inquiry and Word Count (LIWC) tool, we analyze differences in language patterns between conflict and non-conflict interactions. One hackathon team studied was composed of strangers, and the other of pre-existing teammates, allowing us to examine the role of familiarity in conflict communication. Our findings reveal distinct linguistic characteristics during conflict, including reduced negative emotion and prosocial language, as well as differences in sentence complexity and cohesion based on team familiarity. These insights contribute to the growing body of research on team dynamics and could inform future tools for real-time conflict detection and resolution in collaborative design settings.
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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 | 0.006 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".