Computational Models of Linguistic Alignment for Clustering Group Participants and Predicting Task Outcomes
Bibliographic record
Abstract
This work examines the relationships between several measures of linguistic alignment in task-based group conversation, and assesses how useful these measures are for predicting task performance and participant affect. The study is carried out using human-human conversational data, with potential implications for human-AI conversations where an artificial agent can decide if and how to align itself linguistically with human subjects. We implement several alignment measures including long-term measures that assess the level of convergence over the course of the conversation, as well as short-term coordination measures that have been related in previous research to power dynamics. The study uses two publicly available English-language survival task datasets. After analyzing correlations between the various linguistic alignment measures, we perform clustering in order to unveil the main types of alignment patterns that are prevalent in the data. Finally, we use the alignment measures as machine learning features to predict participant task performance and participant affect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".