“Is This Even Relevant?” Investigating the Relevance of Antecedents to Trust in Ad Hoc Dyads
Bibliographic record
Abstract
Trust is an important variable for effective ad hoc collaboration. As ad hoc collaborations become more prevalent, researchers and stakeholders will need to identify what features facilitate rapid and appropriate swift trust, particularly in contexts comprising salient risk and high stakes. The present work investigated the relevance and impact of antecedents to swift trust in ad hoc dyads. In a within-subjects experiment, we leveraged a vignette and assessed what antecedents were relevant and affected trust in ad hoc dyad formation. The results showed that antecedents varied in terms of their relevance and effect on trust. We discuss how these results align with extant research and implications for future research investigating swift trust in ad hoc collaborations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.010 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".