Designing for Interpersonal Trust – The Power of Trust Tokens
Bibliographic record
Abstract
Computer mediated communication systems (CMCSs) are increasingly being used to support activities of virtual teams, improving information exchange and capacity of these teams. However, the technology that enables these teams to benefit from effective and timely information exchange suffers from constraints of media richness, limiting the amount of social information that can be transmitted. This missing information can affect the formation of trust between members of these teams. In this paper, we conducted an ethnographic study to identify behaviors that facilitate the development of trust within face-to-face teams as an opportunity to design interface design objects with similar effects. A key observation was trust tokening, where trust is conveyed through social referents. Trust tokening has the potential to be adapted for use in computer support systems where establishing interpersonal trust is just as important as face-to-face collaborations.
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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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".