Feature Analysis: A Method for Analyzing the Role of Ideology in App Design
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
Many apps are designed to solve a problem or accomplish a task, such as managing a health condition, creating a to-do-list, or finding work. The solutions that app developers offer reflects how they believe that users and other stakeholders understand the problem. Each individual developer may have different ideas but analyzing many apps together can reveal the average or typical ways that developers in the set think about the problems that their apps are designed to solve. Building on content analysis, interface analysis, the concept of affordances, and speculative design, this article offers a new method that we call “feature analysis” to analyze what a set of apps designed to solve the same problem can tell us about the relationship between app design and ideology. By counting and classifying the features in a set of apps, feature analysis enables researchers to systematically answer questions about how app developers’ design choices reflect existing cultural norms, assumptions, and ideologies.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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