Levels of Automation in Human Factors Models for Automation Design: Why We Might Consider Throwing the Baby Out With the Bathwater
Bibliographic record
Abstract
This paper responds to Kaber’s reflections on the empirical grounding and design utility of the levels-of-automation (LOA) framework. We discuss the suitability of the existing human performance data for supporting design decisions in complex work environments. We question why human factors design guidance seems wedded to a model of questionable predictive value. We challenge the belief that LOA frameworks offer useful input to the design and operation of highly automated systems. Finally, we seek to expand the design space for human–automation interaction beyond the familiar human factors constructs. Taken together, our positions paint LOA frameworks as abstractions suffering a crisis of confidence that Kaber’s remedies cannot restore.
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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 | 0.036 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".