Bibliographic record
Abstract
This article describes one part of a human factors study conducted over 3 months in a petro-chemical manufacturing plant in Australia. The project had two purposes, namely, to identify issues to be included in a training course for plant operators and to identify low-level usability-related software issues that might be rectifiable prior to system implementation. After interviewing 28 operators and eight managers, the operators were observed on the job while interacting with the old system. Finally, the 3-part usability assessment comprising 2 expert inspections and a user-based quasi-walkthrough was conducted. As the study took place shortly before a new, off-the-shelf automated manufacturing system was implemented, it was not possible to test an interactive version, relying instead exclusively on static screens. This made it impossible to provide user performance data, which could have helped to convince management of the seriousness of certain problems. One of these proved so severe that an engineer had to be present 24/7 in the control room for 6 months following system cutover because the operators were unable to achieve the required product quality. Based on the data, suggestions are made for expanding the usability construct to include assessment of perceived technology usefulness and to refine the concept of attitude in mandatory settings.
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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".