Selecting Methods for the Analysis of Reliance on Automation
Bibliographic record
Abstract
Human reliance on imperfect automation has been the subject of many laboratory experiments. Across these studies, a diverse set of indices of reliance have been used, even in studies with similar experiment settings. This inconsistency of reliance measures makes the meta-analysis of experimental findings difficult. Moreover, few researchers have rigorously defined the optimal level of automation reliance in their settings, making it difficult to judge the appropriateness of that reliance. This paper attempts to guide researchers in selecting and interpreting measures of automation reliance behavior. We review the reliance analysis methods in existing research and propose four criteria for selecting among them. It is recommended that, where possible, future studies define optimal reliance to make unequivocal judgment about the appropriateness of reliance. In addition, more reliable and insightful conclusions can be obtained through the use of multiple measures.
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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 | 0.267 | 0.585 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.032 | 0.026 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".