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
Usage data logged from user interactions can be extremely valuable for evaluating software usability. However, instrumenting software to collect usage data is a time-intensive task that often requires technical expertise as well as an understanding of the usability issues to be explored. We have developed a new technique for software instrumentation that removes the need for programming. Interactive Usability Instrumentation (IUI) allows usability evaluators to work directly with a system's interface to specify what components and what events should be logged. Evaluators are able to create higher-level abstractions on the events they log and are provided with real-time feedback on how events are logged. As a proof of the IUI concept, we have created the UMARA system, an instrumentation system that is enabled by recent advances in aspect-oriented programming. UMARA allows users to instrument software without the need for additional coding, and provides tools for specification, data collection, and data analysis. We report on the use of UMARA in the instrumentation of two large open-source projects; our experiences show that IUI can substantially simplify the process of log-based usability evaluation.
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 | 0.000 | 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 | 0.000 | 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