The Absence of Degree of Automation Trade-Offs in Complex Work Settings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to test the predictions of the routine-failure trade-off (or lumberjack) model in a full-scope simulator study with expert operators performing realistic control tasks. BACKGROUND: A meta-study of degree of automation (DOA) studies concluded that DOA predicts task performance under both routine and automation failure conditions, workload, and situation awareness. Empirical support for this conclusion appears to be weak for complex work situations. METHOD: A full-scope nuclear power plant simulator experiment was conducted in which licensed operating crews completed realistic procedure execution tasks. Dependent measures selected from the lumberjack model were collected and analyzed for systematic effects. RESULTS: Situation awareness increased with increasing DOA, which contradicts the lumberjack model. Anticipated workload and failure task performance effects were not observed. CONCLUSION: The experimental results add further evidence challenging the applicability of the lumberjack model to complex work situations. APPLICATION: Practitioners should use caution when extending the predictions of the lumberjack model based on data from simple work situations to complex work situations. Researchers should invest more resources in testing the predictive power of the lumberjack model in complex work situations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it