Characterizing large-scale use of a direct manipulation application in the wild
Bibliographic record
Abstract
Examining large-scale, long-term application use is critical to understanding the degree to which an application meets the needs of its user community. However, there has been limited published analysis of this type of data, none of which pertains to applications that support creating and modifying content using direct manipulation. In this paper, we present an analysis of 2 years of usage data from an in-strumented version of the GNU Image Manipulation Pro-gram, including data from over 200 users. In the course of our analysis, we contribute to the body of knowledge on large-scale application use in three ways. First, we show that previous findings concerning the sparseness of com-mand use and idiosyncrasy of users ’ command vocabularies extend to a new domain and interaction style. Second, we demonstrate that direct manipulation applications require new analysis methods to determine command popularity. Finally, we describe the novel application of a clustering technique to characterize users ’ higher-level tasks. Author Keywords Logging, long-term usage, community command usage, open source software, remote usability, adaptive interfaces, longitudinal study ACM Classification Keywords H5.m. Information interfaces and presentation (e.g., HCI):
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.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".