Tracking menopause: An SDK Data Audit for intimate infrastructures of datafication with ChatGPT4o
Bibliographic record
Abstract
This article presents a novel methodology to examine the tracking infrastructures that extend datafication across a sample of 14 menopause-related applications. The Software Development Kit (SDK) Data Audit is a mixed methodology that explores how personal data are accessed in apps using ChatGPT4o to account for how digital surveillance transpires via SDKs. Our research highlights that not all apps are equal amid ubiquitous datafication, with a disproportionate number of SDK services provided by Google, Meta, and Amazon. Our three key findings include: (1) an empirical approach for auditing SDKs; (2) a means to account for modular SDK infrastructure; and (3) the central role that App Events—micro-data points that map every action we make inside of apps—play in the data-for-service economy that SDKs enable. This work is intended to open up space for more critical research on the tracking infrastructures of datafication within our apps in any domain.
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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 | 0.017 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".