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
Open source projects are gradually incorporating usability methods into their development practices, but there are still many unmet needs. One particular need for nearly any open source project is data that describes its user base, including information indicating how the software is actually used in practice. This paper presents the concept of open instrumentation, or the augmentation of an open source application to openly collect and publicly disseminate rich application usage data. We demonstrate the concept of open instrumentation in ingimp, a version of the open source GNU Image Manipulation Program that has been modified to collect end-user usage data. ingimp automatically collects five types of data: The commands used, high-level user interface events, overall features of the user's documents, summaries of the user's general computing environment, and users' own descriptions of their planned tasks. In the spirit of open source software, all collected data are made available for anyone to download and analyze. This paper's primary contributions lie in presenting the overall design of ingimp, with a particular focus on how the design addresses two prominent issues in open instrumentation: privacy and motivating use.
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 | 0.000 |
| 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