Cost-Justifying Investments in Advanced Human-Machine Interface Technologies I: A Cost-Benefit Framework for the Process Industries
Bibliographic record
Abstract
Advanced human-machine interfaces (HMIs), such as those developed under the ecological interface design framework, continue to show substantial advantages for supporting effective operator control (Vicente, 2002). However, prospective industry users of these technologies have expressed a need to make a quantitative economic case for designing and implementing advanced HMIs. To date, no comprehensive cost justification study has been published on advanced HMIs. The purpose of this paper is to report original work on a cost-benefit framework for advanced HMI's. We first present a brief summary of findings from a literature review of existing human factors cost justification studies and present a working list of metrics. We then describe a general cost-benefit framework derived from this list of metrics and discuss issues to be considered in applying this model in practice.
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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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".