Selecting a suitable technology: it's about people and their tasks
Bibliographic record
Abstract
This paper reports the outcome of a user-context analysis of two interactive devices used by product assemblers in a large grocery distribution warehouse in Ottawa, Canada. One used a screen-based textual platform and a handheld device, and the other was speech-based. The former had been in use in the centre for some time, but management was trialling hands-free Interactive Voice System (IVS) at the time the study took place, to help them decide if the hand-held display units should be phased out throughout the centre. The IVS was a small battery-operated computer with scanning capabilities that acted as an interface to the backend Warehouse Management System (WMS) and the user. The hand-held device had a keypad and a barcode reader for data entry as well as a small screen display. Two versions of this technology were in use at the time, displaying either six or eight lines of text in a serif font. Both displays used a serif font. Findings showed that the main problems were less with the technologies than with work-related user performance requirements that revealed certain negative effects outlined in the paper. It was concluded that user experience theories and models in the current literature were inadequate for guiding the research, and that the HCI community needs to adopt a more nuanced approach to the definition and measurement of the user experience construct.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".