Swift Trust in Distributed Ad Hoc Teams
Bibliographic record
Abstract
Swift trust is trust developed quickly even without direct and personal experience with another person and has been increasingly posited in the literature to be one way in which members of ad hoc teams can quickly form trust (Meyerson, Weick & Kramer, 1996). This pilot study explored whether the regimental identity of teammates could influence levels of "swift" trust within teams. The secondary focus of this experiment was the impact of potential trust violations. Twenty-four teams of Canadian Forces (CF) reservists each conducted four tactical assault missions in a first-person gaming laboratory. Each 4-person team was composed of 2 CF personnel and 2 confederate researchers (purported to be CF personnel). Members of the team worked in a simulated distributed environment (separated by partitions), and were initially introduced to each other only using a 1 page written profile that described their background and operational experience. Their task in the computer game was to operate as 2 separate fire teams approaching the target area from 2 different sides in order to engage and destroy terrorists. Teammates communicated via radio only but interacted within the simulated mission area through their computer avatars. In order to manipulate regimental identity, the 2 confederate members of the newly formed and distributed team were reported to come from either the same regiment or a different regiment as the actual CF participants. In addition, to investigate whether trust violations affected the development of trust over the four missions, in half of the missions, a confederate team member performed a behavior that could put the team at risk. Questionnaires assessed the impact of regimental identity and potential trust violations on levels of team trust before the mission began (pre-mission), during a mission freeze (about 5 min into the mission) and at the end or post-mission.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".