Adapting the Locales Framework for Heuristic Evaluation of Groupware
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
Heuristic evaluation is a rapid, cheap and effective way for identifying usability problems in single user systems. However, current heuristics do not provide guidance for discovering problems specific to groupware usability. In this paper, we take the Locales Framework and restate it as heuristics appropriate for evaluating groupware. These are: 1) Provide locales; 2) Provide awareness within locales; 3) Allow individual views; 4) Allow people to manage and stay aware of their evolving interactions; and 5) Provide a way to organize and relate locales to one another. To see if these new heuristics are useful in practice, we used them to inspect the interface of Teamwave Workplace, a commercial groupware product. We were successful in identifying the strengths of Teamwave as well as both major and minor interface problems.
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.
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 | 0.029 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.050 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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