The misapplication of engineering models to business decisions
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
The HCI community has long been accused of delivering 'common sense', 'useless' information, and to be ignorant of business needs. HCI experts are also criticized for failing to provide or apply theory-based techniques. This paper shows that the two goals may be incompatible. It discusses one case study in which HCI data intended for one purpose were inappropriately applied to support another. Theory-driven GOMS (Goals, Operators, Methods, Selection rules) models generated to predict performance in two competing applications were subsequently used for making a business decision. Three similar data-driven studies designed to inform a business decision are then presented. Findings from all these studies demonstrate that the parameters on which the business decision based on GOMS data was made were largely irrelevant to that decision. It is argued that HCI experts must learn to relate their findings to business needs and values if HCI practice is to progress.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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