The Effects of Linguistic Qualifiers and Intensifiers on Group Interaction and Performance in Computer-Supported Collaborative Argumentation
Bibliographic record
Abstract
This study tested the effects of linguistic qualifiers and intensifiers on the number and types of replies elicited per argument and per challenge posted in online debates. To facilitate collaborative argumentation, thirty-two students (22 females, 10 males) enrolled in a graduate-level online course classified and labeled their messages as arguments, challenges, supporting evidence, or explanations prior to posting each message. The findings showed that qualified arguments elicited 41 percent fewer replies (effect size = -.64), and the reduction in replies was greatest when qualified arguments were presented by females than males. Challenges without qualifiers, however, did not elicit more replies than challenges with qualifiers. These findings suggest that qualifiers were used to hedge arguments, and such behaviors should be discouraged during initial stages of identifying arguments (more so in all-female than in all-male groups) in order to elicit more diverse and more opposing viewpoints needed to thoroughly and critically analyze arguments. Keywords: Computer-mediated communication, CMC, communication style, group interaction patterns, interaction analysis, computer-supported collaborative learning, CSCL, collaborative argumentation.
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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 | 0.008 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".