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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.073 | 0.240 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".