Adapting the Locales Framework for Heuristic Evaluation of Groupware
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.
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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 | 0.023 | 0.097 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".