Stumbling Blocks and Alternative Paths: Reconsidering the Walkthrough Method for Analyzing Apps
Bibliographic record
Abstract
The walkthrough method was developed as a way to trace an app or platform’s technological mechanisms and cultural references to understand how it guides users. This article explores the method’s enduring strengths and emergent weaknesses regarding technological advances and developments in app studies. It engages with adjacent methods for understanding apps’ intensifying structural and economic complexity, datafication, algorithmic logic, and personalization as well as approaches fostering a feminist ethics of care toward users. Considering these perspectives, the article discusses challenges encountered in teaching the method and applying it to algorithmically driven apps. With TikTok as a central example, examining the walkthrough process demonstrates the method’s incongruence for investigating several aspects of the app, especially its automated personalization. These challenges highlight the need to combine, supplement, or exchange the method with other approaches as part of an expanding and flexible toolkit of methods in app studies.
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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 | 0.038 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".