The Effects of Design Features on Users’ Trust in and Reliance on a Combat Identification System
Bibliographic record
Abstract
In a previous study, we found that users’ trust in and reliance on an individual combat identification system is influenced by the system’s reliability as well as users’ awareness of the reliability. In this exploratory study we test the effects of design features of the same system on users’ target identification performance as well as their trust in and reliance on the system. In a simulated task environment, we varied the automation activation mode (i.e., automatic vs. manual) and the presentation of the “unknown” feedback (i.e., explicit vs. implicit). Participants responded fastest when the “unknown” feedback was provided automatically with explicit indication. In addition, participants trusted the explicit “unknown” feedback more than the implicit feedback. However, neither reliance behavior nor identification accuracy changed significantly across conditions. This study has implications for the design of combat identification systems to achieve appropriate trust. In addition, the results suggest that when studying trust in automation using a simulation, it is important to simulate the design features.
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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 | 0.008 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".