ANALYZING PLATFORM POWER: APP STORES AS INFRASTRUCTURAL PLATFORM SERVICES
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
This paper examines how platform power is operationalized in the specific case of the iOS App Store. We take a first step in developing an analytical framework that critically examines the infrastructural power relations that constitute online platform ecosystems. Building on a relational understanding of power, we propose an analytical vocabulary to systematically interrogate the material power relations among the three main actors active in platform ecosystems: platform operators (e.g. Apple), third party institutions (e.g. app developers, businesses, governments), and end-users (i.e. individuals). To better differentiate among these three different actors in platform ecosystems, the paper proposes to study platform power at five expanding levels, similar to those of ecological ecosystems: individual actors, infrastructural platform services, company platform ecosystems, geopolitical platform ecosystems, and the global platform ecosystem. Studying infrastructural platform services, such as app stores, offers relevant insight into how globally operating platforms are able to set, steer, and bend rules and norms that impact individual actors on the local and national level. In the case of app stores, the paper shows that platform power is not casual or discursive, but highly strategic, uniform, and centralized. By interrogating the operationalization of platform power at the platform service level, the paper demonstrates that platform power is not a property of one platform itself, but a corollary of a platform’s function in the context of other platforms and actors in a dynamic ecosystem.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it