Evaluating Early Prototypes in Context: Trade-offs, Challenges, and Successes
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
We would like to say that experience prototyping and Wizard of Oz prototyping, taken together, constitute a suitable alternative to true contextual evaluation for early prototypes, one gaining realism at the expense of impartiality, the other impartiality at the expense of realism. As our experiences show, however, each pervasive application design poses unique challenges that you must consider when applying these approaches. Regardless, it's often a good trade-off to sacrifice some measure of realism to evaluate early prototypes. Evaluation of early pervasive computing prototypes in context is a pragmatic exercise, but one that is nonetheless informed by general approaches that reflect and adapt to the challenges of pervasive application development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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