An Exploratory Study of the Relationship between Defensive and Supportive Talk, Verbal Aggressiveness and Communication Climate
Bibliographic record
Abstract
Significant research has investigated Jack Gibb’s model of defensive and supportive communication, but little has explored the influence of the type of talk -- defensive or supportive -- on perceptions of communication climate and the role that verbal aggressiveness may play in influencing both the types of talk and these perceptions. This thesis explored the relationship between defensive and supportive talk, verbal aggressiveness and communication climate using a mixed-method approach. Specifically, the Verbal Aggressiveness Scale was used to group participants for a dyadic problem solving exercise which generated conversational data that was analyzed qualitatively. Then, the Communication Climate Inventory was used to measure participants’ perceptions of the communication climate that emerged in their problem-solving dyad. The findings highlight factors that may influence the perception of communication climate. Examples of supportive talk that builds positive communication climates and limits the effects of verbal aggressiveness and examples of defensive talk that leads to negative communication climates are provided. This research demonstrates that language has an influence on communication climate through the words that shape the complex ways people perceive and understand each other and, interestingly, that the negative impact of defensive communication overrides the positive impact of supportive communication on the emergent communication climate.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".