Effect of non-human avatars on opinion convergence in remote spoken interactions
Bibliographic record
Abstract
Opinion convergence in spoken interactions is influenced by various factors, such as interlocutor traits, social identity, and context [Pardo, 2022, JPhon 95]. Previous studies on communication via Zoom conferences have reported that differences in opinion can affect visual cues in vowel productions [Ma et al., 2023, HISPCSL]. However, there is a general lack of understanding of how technology-mediated communication affects opinion convergence. This study investigated whether the availability of visual cues in online video communication has an effect on opinion convergence between people. We collected data from fifty college students (aged 18–30, with an average age of 19.96) who were randomly paired into 25 groups to participate in a 20-min Zoom discussion on a range of debatable propositions. The discussions were conducted using two conditions: no camera (black screen) or fox avatars set up by Zoom. Participants' opinions on the propositions were measured before and after the discussion. We will measure whether a difference in avatar vs. non-avatar conditions impacts the level of opinion convergence between pairs of speakers by comparing the changes in the levels of convergence between before and after conversations. Implications for the general effects of technology-mediated communication on opinion formation and convergence will be discussed.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".